This role will serve as the connective tissue between global product marketing and regional go-to-market teams, ensuring Cresta's platform is positioned, evangelized, and activated in ways that resonate with local buyers, markets, and competitive dynamics.

In this highly strategic and cross-functional role, you will act as both the voice of the region and the voice of the platform, translating global strategy into regionally relevant narratives while bringing market insights back to shape positioning, campaigns, and product direction.

Responsibilities:

• Own regional positioning and narrative for EMEA and APAC, adapting Cresta's global platform story into clear, compelling, and locally resonant messaging

• Serve as the primary product marketing partner to regional Sales, Marketing, and Customer Success leaders

• Bring new international capabilities (e.g., language support, regional features) to market, ensuring the field is equipped with clear positioning, messaging, and supporting collateral

• Act as a regional evangelist for Cresta's platform in customer meetings, events, and executive engagements

• Partner with global product marketing to refine positioning, messaging, and launches based on regional insights

• Partner with global customer marketing to secure case studies, customer advocacy and CAB participation amongst the International Cresta customer community

• Partner with global marketing on regional research-driven content production

• Enable field teams with narratives, content, and competitive intelligence to drive pipeline growth

• Collaborate with demand generation and regional marketing to align campaigns with market nuances

• Develop scalable regional enablement frameworks for sales and customer-facing teams

• Analyze regional performance across pipeline, conversion, and win/loss to improve go-to-market effectiveness

• Act as a bridge between global and regional teams to balance consistency and localization

Qualifications:

• 8–10+ years of product marketing experience in Enterprise SaaS or AI-driven technology

• Experience supporting go-to-market strategy across EMEA and/or APAC

• Strong narrative-building and storytelling skills for complex platforms

• Proven ability to partner with sales and drive revenue impact

• Excellent communication and executive presentation skills

• Highly analytical and data-driven

• Strong cross-functional leadership skills

• High cultural awareness across international markets

Preferred Qualifications:

• Experience in contact center or customer experience technology

• Familiarity with AI, large language models, or agentic systems

• Experience in high-growth or scale-up environments

Perks & Benefits:

We offer a comprehensive and people-first benefits package to support you at work and in life:

• Comprehensive medical, dental, and vision coverage with plans to fit you and your family

• Flexible PTO to take the time you need, when you need it

• Paid parental leave for all new parents welcoming a new child

• Retirement savings plan to help you plan for the future

• Remote work setup budget to help you create a productive home office

• Monthly wellness and communication stipend to keep you connected and balanced

• In-office meal program and commuter benefits provided for onsite employees

Compensation at Cresta:

Cresta's approach to compensation is simple: recognize impact, reward excellence, and invest in our people.

We offer competitive, location-based pay that reflects the market and what each individual brings to the table.

The posted base salary range represents what we expect to pay for this role in a given location.

Final offers are shaped by factors like experience, skills, education, and geography.

In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family.

Salary Range: $140K-$170K GBP + Equity

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We're seeking a Member of Technical Staff to pioneer the integration of vision-language models (VLMs) into our FLUX stack. As a key member of our team, you'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet.

What You'll Work On

• Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack , innovating on architectures, not just applying existing ones
• Design fine-tuning strategies that adapt VLMs to specialized creative use cases (captioning, editing instructions, prompt enhancement) that general-purpose models can't handle
• Research integrations between VLM/LLM capabilities and our diffusion and flow pipelines , finding creative ways to improve generation quality and controllability without computational bottlenecks
• Evaluate emerging multimodal architectures, translating the best of recent research into practical improvements

What We're Looking For

• You've pretrained or significantly advanced a VLM (not just SFT'd or LoRA'd one) that was deployed in a production system or released publicly
• Strong publication record or unambiguous production track record showing you push the frontier on multimodal architectures
• Deep understanding of how vision and language representations interact: tokenization, alignment, grounding, cross-modal attention, and the failure modes of each
• Experience with distributed training at multi-node scale
• Comfortable at the research/production boundary , you care whether the work ships and generalizes, not just whether it reads well
• Experience with diffusion or flow-based generative models is a strong plus , especially if you've thought about how autoregressive and diffusion paradigms can compose

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all.

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Our ideal candidate will have a strong foundation in computer graphics, computational geometry, or mathematics, and experience with AI/ML techniques as applied to 3D geometry processing. You will work closely with a multidisciplinary team to integrate novel solutions into existing meshing and simulation software.

Key responsibilities include:

• Researching and designing advanced AI/ML algorithms for 3D geometry processing
• Developing methods for shape classification, feature identification, and semantic model understanding
• Collaborating with a multidisciplinary team to integrate novel solutions into existing meshing and simulation software
• Leveraging and contributing to VLMs (Vision-Language Models) with embedded 3D geometry data for enhanced model analysis
• Participating in all stages of the product lifecycle, including code planning, prototyping, development, testing, and evolution
• Engaging in peer code reviews, technical discussions, and knowledge-sharing sessions to drive continuous improvement

The successful candidate will have a strong analytical and problem-solving skills, with the ability to tackle complex technical challenges. You will also have excellent communication skills, with the ability to articulate complex ideas to diverse audiences.

If you are a motivated and innovative individual with a passion for AI/ML and 3D geometry processing, we encourage you to apply for this exciting opportunity.

Benefits:

• Comprehensive medical and healthcare plans
• Time away from work for vacation and sick leave
• Family support, including maternity and paternity leave
• ESPP (Employee Stock Purchase Plan)
• Retirement plans
• Competitive salary and bonuses

At Synopsys, we value diversity and inclusion, and we are committed to creating a workplace that is welcoming and inclusive for all employees. We believe that a diverse and inclusive workplace is essential for driving innovation and success.

If you are interested in this opportunity, please submit your application, including your resume and cover letter, to [insert contact information]. We look forward to hearing from you!

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At Ansys, Part of Synopsys, we're the global leader in engineering simulation software, helping innovative companies solve complex design challenges. Our cutting-edge solutions power advancements across industries, from aerospace to consumer electronics.

You Are:

You are a skilled engineer passionate about creating AI-driven solutions that elevate user productivity. With a strong background in computer science, AI, and machine learning, you bring practical experience working with Large Language Models and scripting in Python. You thrive on solving complex problems, communicate clearly, and collaborate effectively across teams. Motivated, detail-oriented, and eager to learn, you enjoy turning innovative ideas into reliable, high-impact product features.

You approach challenges with curiosity and resilience, always looking for ways to innovate and improve. Comfortable with change, you adapt quickly to new technologies and requirements. You bridge the gap between theory and real-world application, ensuring AI solutions make a meaningful impact. Collaborative by nature, you value diversity and continuous learning, helping to build a positive and high-performing team culture. Your passion for AI drives you to deliver features that empower users and transform engineering tools through intelligent automation.

What You'll Be Doing:

• Designing and implementing robust, end-to-end agentic workflows within the Ansys Mechanical product using cutting-edge AI technologies.
• Analyzing real-world usage data to iteratively improve workflow effectiveness and user experience.
• Developing and optimizing embedding pipelines and integrating with vector databases to achieve high-performance, cost-effective AI systems.
• Creating, refining, and deploying high-quality Large Language Model prompts to deliver impactful AI-powered user interactions.
• Deploying AI workflows to local networks, ensuring secure and reliable integration into existing infrastructure.
• Investigating and resolving defects in production code, enhancing software reliability and user trust.
• Developing comprehensive unit tests for new features, ensuring maintainable and error-resistant codebases.
• Analyzing results from AI workflows to drive continuous improvements in software usability, reliability, and performance.

The Impact You Will Have:

• Transforming advanced AI and agentic frameworks into tangible product features that elevate user productivity and satisfaction.
• Driving the adoption of innovative AI technologies within Synopsys' Mechanical Core, setting new standards for intelligent product design.
• Accelerating the development and deployment of high-impact AI solutions, directly influencing the capabilities of next-generation engineering tools.
• Enhancing the reliability and efficiency of AI-powered workflows, reducing operational friction for end users.
• Contributing to a culture of excellence, collaboration, and continuous learning within a high-performing engineering team.
• Enabling Synopsys to maintain its leadership in delivering intelligent, scalable, and future-ready technology solutions to a global market.

What You'll Need:

• A bachelor's degree in Computer Science or a related field, ideally with coursework in Artificial Intelligence and Machine Learning with 2+ years of hands-on experience is a plus.
• Demonstrable experience working with Large Language Models in production environments.
• Proficiency in scripting languages, especially Python, for rapid prototyping and workflow automation.
• Strong analytical and algorithmic problem-solving skills, with a focus on scalable and efficient solutions.
• Experience with unit testing, debugging, and code optimization in complex software systems.
• Familiarity with development tools such as Microsoft Visual Studio, Git, and GitHub.
• Exposure to NLP, Knowledge Graphs, and agentic workflows (reasoning, tool usage, automated task execution, Model Context Protocols).
• Knowledge of additional programming languages such as C++, C#, Go, Rust, or Java is a plus.

Who You Are:

• An innovative and adaptable engineer with a growth mindset.
• Excellent written and verbal communicator, able to collaborate across teams and present complex ideas clearly.
• A responsible, motivated team player who takes ownership and delivers results.
• Detail-oriented with a passion for quality and continuous improvement.
• Curious and proactive, always seeking new ways to solve problems and add value.

The Team You'll Be A Part Of:

You will join the Ansys Mechanical team,a dynamic, collaborative group of engineers dedicated to advancing the next generation of AI-powered agentic platforms. The team focuses on integrating state-of-the-art AI systems into the Ansys Mechanical product suite, working closely with product managers, designers, and other engineering teams to deliver innovative features that meet real-world user needs. You will find an environment that values creativity, technical rigor, and continuous learning.

Rewards and Benefits:

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

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AI Program Management, Principal

Bengaluru, Karnataka, India

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Hire Type EmployeeJob ID 15009Date posted 02/17/2026

A peek inside our office

Po Popal

Workplace Resources, Sr Director

We Are:

At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future through continuous technological innovation.

You Are:

You are a highly skilled program manager with strong understanding of Artificial intelligence technology and its transformative potential. With deep expertise in leading complex, cross-functional programs, you thrive in fast-paced environments where strategic thinking and execution go hand in hand. You are comfortable collaborating with diverse stakeholders, from engineers to executive leadership, and excel at aligning program objectives with organisational strategy.

You bring a proven track record of managing enterprise-scale AI initiatives, including the evaluation and integration of new models, tools, and third-party solutions. Your leadership style fosters a culture of continuous improvement, and you are adept at change management, ensuring smooth adoption of GenAI technologies across corporate functions. If you are energised by the challenge of shaping the future of AI at scale, Synopsys offers you the platform to make a lasting impact.

What You’ll Be Doing:

• Drive portfolio-level prioritisation and delivery of Gen/Agentic AI initiatives, ensuring focus on highest-value outcomes.

• Ensure alignment between program execution and organisational goals, maximising business value from AI investments.

• Contribute strongly towards cross-functional AI governance and decision forums that balance innovation velocity with enterprise risk and compliance.

• Shape the future of the Gen/Agentic AI solutions, influencing the direction of AI governance, risk management, and compliance efforts.

• Enhance cross-functional collaboration and knowledge sharing, breaking down silos to accelerate innovation.

• Build scalable governance frameworks and operationalise industry standards to ensure compliance and quality throughout the AI lifecycle.

• Empower business units to confidently adopt GenAI technologies, driving enterprise-wide transformation and agility.

• Collaborate closely with stakeholders to define program objectives, scope, and deliverables that align with strategic business goals.

• Develop, maintain, and optimise comprehensive program plans, resource allocation, and risk mitigation strategies.

• Monitor program progress, identify potential roadblocks, and implement proactive solutions to keep projects on track.

• Provide strategic guidance and support to project teams, acting as a mentor and thought leader in AI program delivery.

• Foster a culture of continuous improvement within the PMO function, incorporating best practices and lessons learned.

• Stay current with industry trends, regulatory developments, and advancements in AI to enhance program delivery and governance.

• Drive the successful delivery of GenAI offerings and strategic initiatives, positioning Synopsys as a leader in enterprise AI adoption.

• This onsite role is based in Bangalore and requires regular collaboration with US-based teams, including overlap with Pacific Time hours.

What You’ll Need:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related discipline.

• Proven experience as a Program Manager, leading complex projects in the software or high-tech industry and leading end-to-end evaluation of new model/app/tool requests (including third-party solutions), coordinating risk reviews across IP, privacy, security, and legal/compliance.

• Robust understanding of Generative AI (GenAI), Large Language Models (LLMs), Agentic AI, Responsible AI, and AI Governance, and are adept at embedding responsible practices, such as fairness, transparency, privacy, and accountability,into every stage of program delivery.

• Awareness of industry standards such as the NIST AI Risk Management Framework and EU AI Act-style Quality Management Systems, and ability to operationalise them effectively within large organisations.

• Hands-on experience with portfolio management tools (e.g., Jira, Azure DevOps), intake workflow systems, and knowledge bases (e.g., SharePoint, Confluence).

• Ability to drive Scalable, auditable AI governance mechanisms adopted across multiple business units.

• Reduced friction and cycle time for AI model/app approvals without increasing risk

• Measurable enterprise adoption and business value from GenAI deployments

Who You Are:

• Strategic thinker with strong analytical and problem-solving skills.

• Excellent communicator, able to convey complex ideas clearly to technical and non-technical stakeholders.

• Adaptable and resilient in the face of change, with a continuous improvement mindset.

• Collaborative, inclusive, and able to build strong relationships across diverse teams and geographies.

• Comfortable operating in areas of ambiguity where policy, technology, and business objectives intersect.

• Able to manage multiple priorities in a dynamic environment

The Team You’ll Be A Part Of:

You will be part of GenAI Center of Excellence, a team that is passionate about driving innovation and advancing our AI Platform & Solutions. This role reports to Head of AI Program Management & Chief of Staff, GenAI Center of Excellence. We are a close-knit, collaborative group that values open communication and continuous learning. We collaborate closely with engineering, product, compliance, and executive teams to deliver impactful results and shape the company’s AI strategy.

Rewards and Benefits:

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

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The team partners closely with Technology and Engineering teams to design and deliver high-impact data and visualisation tools for COO and Distribution stakeholders. You will also collaborate with internal technology teams on infrastructure, tools, processes, standards, and development practices, as well as work alongside data science and analytics teams across the firm.

The successful candidate will bring a strong passion for technology, data, and client outcomes, with comfort working across a broad range of technical capabilities, including databases, software development, and cloud infrastructure. This role suits someone who enjoys solving complex problems and building scalable, high-impact data products.

At BlackRock, we value curiosity, continuous learning, and professional growth. With over $14 trillion in assets under management, we have a unique responsibility: our products and technology empower millions of investors to save for retirement, pay for education, purchase homes, and improve their long-term financial wellbeing.

Key Responsibilities:

• Explore, profile, cleanse, and preprocess data to ensure high-quality datasets for analytics, reporting, and downstream consumption.
• Design and manage workflows for storing and retrieving vectorised documents to support AI-enabled use cases.
• Apply embedding models to build AI-driven solutions.
• Leverage modern AI and machine-learning techniques, including large language models (LLMs) and agent-based systems, to enhance data workflows and automation.
• Design, build, and maintain scalable ELT pipelines in Snowflake, covering data ingestion, transformation, and publication layers for enterprise use.
• Develop and optimise Snowflake data models (schemas, views, and curated datasets) to enable consistent, performant, and well-governed access.
• Implement robust data quality controls, including validation, reconciliation, monitoring, and alerting, to ensure the accuracy and reliability of critical datasets.
• Partner with central platform and data engineering teams to support Snowflake architecture, including performance tuning, warehouse optimisation, security patterns, and cost-effective usage.
• Write high-quality, maintainable code that is well-tested, documented, and aligned with engineering best practices, including version control and peer review.
• Build and maintain Streamlit applications to enable self-service data exploration, operational tooling, and lightweight analytics for business users, including applications that interact directly with Snowflake datasets and stored procedures.
• Translate business questions into technical solutions, delivering clear outputs and actionable insights for both technical and non-technical stakeholders.

Skills and Competencies:

• Strong experience with Snowflake and advanced SQL, including query optimisation and best-practice analytical data modelling.
• Knowledge of modern AI and machine-learning techniques, including large language models (LLMs) and agent-based systems, embedding modes and document vectorization.
• Experience developing and maintaining data transformation workflows using dbt within Snowflake, including modular modelling, testing, and documentation.
• Proficiency in Python for data engineering and application development, including data processing, orchestration patterns, and reusable components.
• Experience building Streamlit applications, ideally in an enterprise environment, with a focus on usability and integration with Snowflake-backed data products.
• Familiarity with modern data engineering practices, including ELT/ETL patterns, incremental processing, scheduling, observability, and automated testing.
• Strong problems-solving mindset, with the ability to work independently, manage ambiguity, and drive continuous improvement.
• Strong communication skills, with the ability to articulate technical concepts and insights to non-technical stakeholders.
• Fluency in English, both written and spoken.

Experience and Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative discipline.
• Proven experience in data engineering, analytics engineering, or a closely related technical role, ideally within a cloud-based data platform environment.
• Experience working with commercial, sales, or distribution datasets is an advantage.
• 3–5 years of relevant experience in data engineering, or a related field within a multinational or complex organisational environment.

Our benefits:

To help you stay energized, engaged, and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge, and be there for the people you care about.

Our hybrid work model:

BlackRock's hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock:

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children's educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment – the one we make in our employees. It's why we're dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

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As a Field Quality Analyst (AI and systems integration), you will be responsible for designing and managing the development of an AI-powered Quality Issue Management Systems platform that centralizes company-wide Quality issues. You will collect and analyze relevant QI data across many sources to identify and integrate high-value features that enhance platform functionality. You will partner with cross-functional leadership to capture business intelligence needs and translate them into detailed technical specifications.

You will lead the platform transition by collaborating with IT to build, configure, and optimize the QIMS environment within Jira Cloud, ensuring alignment with global quality standards. You will execute rigorous testing and UAT (User Acceptance Testing) protocols to qualify the Jira project, working directly with IT developers to resolve technical bottlenecks.

You will implement AI/Machine Learning integrations to mine historical QIMS data, enabling rapid, automated 'Lessons Learned' retrieval to prevent the recurrence of known issues. You will build a CQ library platform to house CQ Issues, CAPA, CIP projects and leverage AI in notebookLM.

Be Yourself. Be Open. Stay Hungry and Humble. Collaborate. Challenge. Decide and just Do. Share our passion for Equality and the Environment. These are the behaviours and values you'll need for success at Logitech. In this role, you will:

Regular collaboration with a dedicated mentor to refine strategies and assess performance.

Working closely with data, IT, and AI teams to develop scalable analytical solutions.

Communication in English with global teams across different time zones.

Being on-site at the Logitech Hsinchu office.

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You will work on implementing machine learning solutions for safety-related systems, help foster a culture of technical excellence and inclusivity, break down long-term product requirements into iterative deliverable stages, and collaborate with backend and ML engineers, and cross-functional teams to ensure deployment of models at a massive scale.

You will possess or be pursuing a PhD or equivalent in Computer Science, Computer Engineering, Mathematics or a related field with solid fundamentals in one or more of the following: Computer Vision, Large Language Models (LLMs) and Deep Learning.

You will have familiarity with large-scale content understanding problems (e.g., text, image, video classification), experience with cloud-based ML platforms and infrastructure, and strong coding skills with proficiency in one or more programming languages and experience with building large-scale systems.

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The ideal candidate will have a Master's degree in Data Science, Computer Science, Mathematics, Statistics, Finance, or a related field. They will have minimum 3+ years of hands-on experience in data science, analytics, or machine learning roles. They will have strong proficiency in Python (Pandas, NumPy, Scikit-learn) and solid understanding of machine learning workflows. They will have experience with SQL and PySpark for large-scale data processing. They will have familiarity with modern data platforms such as Snowflake and Fabric. They will have experience with Git-based version control systems (e.g., GitHub or Bitbucket). They will have knowledge of AI applications, including prompt-based systems and exposure to Large Language Models (LLMs). They will have strong English communication skills, with the ability to collaborate effectively with global teams and present insights clearly. They will have the ability to work flexible hours to support collaboration across different time zones.

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Responsibilities

• Work directly with enterprise customers,via tickets, Slack, and sometimes calls,to diagnose and resolve their most complex technical and product questions, acting as the “last line of defense” before Product and Engineering step in.

• Leverage Perplexity’s own AI tooling and workflow automations to democratize world-class support at scale, continuously identifying opportunities to replace repetitive tasks with agentic, self-service solutions that feel personalized.

• Build durable relationships with Enterprise users, advising on best practices, capturing structured feedback, and championing customer needs in roadmap discussions.

• Own end-to-end troubleshooting: reproduce issues, isolate root causes, collaborate with engineers, and communicate clear, low-jargon explanations.

• Design and maintain detailed use-case flows, playbooks, and internal runbooks that empower teammates and customers to solve recurring challenges faster.

• Create and update external documentation (FAQs, help center, guides, tutorials) and internal knowledge bases to ensure information is discoverable and up-to-date.

• Track and report support KPIs (response time, CSAT, resolution rates) and propose data-driven process improvements.

• Participate in an on-call rotation,including some holidays or weekends,to guarantee timely global coverage.

Requirements

• Minimum 3+ years in B2B enterprise customer support with exposure to U.S. and E.U. markets, or similar fast-paced tech environments.

• Hands-on experience prompting large-language models, plus a solid grasp of AI fundamentals (tokens, context windows, embeddings, latency vs. cost trade-offs, etc.).

• Ability to translate complex technical concepts,APIs, SSO/SAML, cloud integrations,into clear, actionable guidance for non-technical stakeholders.

• Demonstrated strength in critical thinking, rapid context-switching, and ruthless prioritization when juggling multiple escalations.

• Proficiency with modern support platforms (Intercom, Zendesk, Jira) and basic data-analysis tools (e.g., SQL, Looker, Snowflake).

• Exceptional written and spoken English; business-level fluency in at least one additional language such as Spanish, French, or German is strongly preferred.

• Passion for continuous learning, high ownership, and a “do-what-it-takes” mindset in ambiguous situations.

Bonus Points

• Prior experience supporting AI, search, or knowledge-management products.

• Familiarity with payment platforms (Stripe), enterprise identity (SSO/OAuth), and API integrations.

• Knowledge of enterprise cloud-storage ecosystems (Google Drive, SharePoint, Dropbox) and data-governance best practices.

Why Perplexity?

• Impact at Scale – Your work directly influences hundreds of enterprise clients and millions of end-users at an unprecedented scale to encourage personalized support

• Velocity & Ownership – Ship improvements quickly in a culture that values curiosity, speed, and quality

• Cutting-Edge Tech – Operate at the forefront of applied LLMs and help invent how AI can redefine customer support itself.

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Estimated Base Salary $295K – $445K

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the team

The Safety Systems org ensures that OpenAI’s most capable models can be responsibly developed and deployed. We build evaluations, safeguards, and safety frameworks that help our models behave as intended in real-world settings.

About the role

As frontier AI systems become more capable, they are increasingly able to pursue long-horizon goals, use tools, adapt to feedback, and operate with greater autonomy. These advances create enormous potential benefits, but they also introduce the risk that models may behave in ways that are misaligned, deceptive, or difficult to supervise or contain. Reducing loss of control risk is therefore a core challenge for safely developing and deploying advanced AI systems.

As a Researcher for loss of control mitigations, you will help design and implement an end-to-end mitigation stack to reduce the risk of intentionally subversive or insufficiently controllable model behavior across OpenAI’s products and internal deployments. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as model capabilities, deployment patterns, and threat models evolve.

In this role, you will:

• Design and implement mitigation components for loss of control risk,spanning prevention, monitoring, detection, containment, and enforcement,under the guidance of senior technical and risk leadership.

• Integrate safeguards across product and research surfaces in partnership with product, engineering, and research teams, helping ensure protections are consistent, low-latency, and resilient as usage and model autonomy increase.

• Evaluate technical trade-offs within the loss of control domain (coverage, robustness, latency, model utility, and operational complexity) and propose pragmatic, testable solutions.

• Collaborate closely with risk modeling, evaluations, and policy partners to align mitigation design with anticipated failure modes and high-severity threat scenarios, including deceptive alignment, hidden subgoals, reward hacking, and attempts to evade oversight.

• Execute rigorous testing and red-teaming workflows, helping stress-test the mitigation stack against increasingly capable and potentially subversive model behaviors,such as sandbagging, monitor evasion, exploit-seeking, unsafe tool use, or strategic deception,and iterate based on findings.

You might thrive in this role if you:

• Have a passion for AI safety and are motivated to make cutting-edge AI models safer for real-world use.

• Bring demonstrated experience in deep learning and transformer models.

• Are proficient with frameworks such as PyTorch or TensorFlow.

• Possess a strong foundation in data structures, algorithms, and software engineering principles.

• Are familiar with methods for training and fine-tuning large language models, including distillation, supervised fine-tuning, and policy optimization.

• Excel at working collaboratively with cross-functional teams across research, policy, product, and engineering.

• Have significant experience designing and evaluating technical safeguards, control mechanisms, or monitoring systems for advanced AI behavior.

• (Nice to have) Bring background knowledge in alignment, control, interpretability, robustness, adversarial ML, or related fields.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Responsibilities:

• Develop vehicle-level Logical Schematics and set up Design Transmittal (DT) Structures within the Vehicle Systems Engineering Management - Global Design Transmittal (VSEM-GDT) system.
• Ensure the Generic Fusing Strategy and Generic Grounding Strategy are strictly followed across all designs.
• Execute compatibility reviews, assess Systems Failure Mode and Effects Analysis (FMEA), and ensure compliance with related system requirements in the Ford Engineering Design Environment (FEDE).
• Support Hardware-in-the-Loop (HIL), Final Design Judgement (FDJ), and Design Confirmation Vehicle (DCV) Breadboard setups, including determining feature configurations and variants.
• Verify the Bill of Materials (BOM) to ensure all necessary hardware and components are present for Breadboard builds.
• Update module software using In-Vehicle Software (IVS) or JFrog repositories and configure modules using the Ford Converter Tool (FCT) or End-of-Line (EOL) simulation.
• Perform functional point-to-point testing and lead Design Confirmation Vehicle (DCV) signoff for Vehicle Engineering Validation (VEV) Design Verification (DV) Breadboards.
• Provide module configurations and support Vehicle Software Configuration System (VSCS) development.
• Support Plant Build Kits at facilities such as the New Model Programs Development Center (NMPDC) or Roush by managing software updates and module configurations.
• Execute self-tests and collect bus queries to verify part numbers, configurations, and Diagnostic Trouble Codes (DTCs).
• Be part of new vehicle program launch team and responsible to triage and troubleshoot functional issues during vehicle builds and all pre-production phases, including Engineering Prototype (EPT), Tooling Tryout (TT), Pilot Plant (PP), Manufacturing Process (MP1 & MP2), OK to Buy (OKTB), Post Job 1 (PJ1), and Job 1 plus 90 days (J1+90).
• Support Functional Testing and Key Off Load (KOL) test activities to ensure vehicle system readiness.
• Utilize the Software Bill of Materials (SWBOM) to track software revisions.
• Support End-of-Line (EOL) testing before each build event to validate latest vehicle software package and Vehicle Software Configuration System (VSCS).
• Review and identify "false codes" (phantom Diagnostic Trouble Codes set during software reflashing) to ensure accurate system diagnostics and clean vehicle delivery.

Qualifications:

• Bachelor’s Degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• 3-5 years' experience in automotive Electrical/Electronic (E/E) Systems Integration and Validation.
• 3+ years 'experience with Controller Area Network (CAN), CAN-Flexible Data-Rate (CAN-FD), and Local Interconnect Network (LIN) protocols and messaging.
• 2+ years' experience in Issue Triage and Root Cause Analysis of complex functional failures and system-level defects.

Preferred qualifications include proficiency in analyzing vehicle bus traffic using industry-standard tools such as CANalyzer, CANoe, Diagnostic Engineering Tool (DET), or Wireshark, ability to read and interpret complex Electrical Schematics, wiring diagrams, and network communication databases (.dbc or .arxml), fundamental understanding of vehicle power distribution, including fusing and grounding strategies, familiarity with Failure Mode and Effects Analysis (FMEA) and functional safety principles, demonstrated ability to perform hands-on technical troubleshooting on physical hardware, breadboards, or vehicle prototypes, willingness to support onsite activities at manufacturing plants or prototype build facilities like the New Model Programs Development Center (NMPDC), direct experience with Ford-specific engineering tools including Vehicle Systems Engineering Management - Global Design Transmittal (VSEM-GDT) and Ford Engineering Design Environment (FEDE), expertise in Vehicle Software Configuration System (VSCS) development and module configuration, experience using In-Vehicle Software (IVS) or JFrog for software repository management and the Ford Converter Tool (FCT) for module flashing, advanced knowledge of Automotive Ethernet and complex gateway routing, in-depth knowledge of Automotive End-of-Line (EOL) testing procedures, proven ability to identify and resolve "false codes" or phantom Diagnostic Trouble Codes (DTCs) generated during software reflashing, leadership capability to lead cross-functional triage meetings and translate technical bus data into actionable business reports for leadership, proficiency in data management and tracking using Jira and Confluence, proficiency on coding in Python or C#, and proficiency on using Large Language Models.

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You will analyze how users think, feel, and behave in interaction with AI systems,especially in high-risk contexts such as self-harm, manipulation, coercion, and influence,and translate these insights into decision-ready risk assessments, mitigation strategies, and product guidance.

This role bridges clinical/behavioral expertise and intelligence analysis, turning psychological signals and patterns into structured judgments, early indicators, and actionable recommendations.

You will partner closely with investigators, engineers, policy, and trust & safety teams to shape how we understand and mitigate potential risks in human-AI interactions.

Key responsibilities include:

• Developing insights into how AI systems are used in complex or high-risk situations (e.g., self-harm, suicidal ideation, substance-use escalation, and threats of violence), identifying recurring patterns and emerging trends that help guide product, safety, and policy decisions.

• Synthesizing behavioral, psychological, and intelligence signals into clear narratives about user needs, system dynamics, and potential areas of risk or vulnerability.

• Producing decision-ready briefs and assessments that inform product, safety, and policy decisions.

• Developing and refining behavioral risk frameworks, taxonomies, and indicators (e.g., severity models, escalation pathways, psychological harm categories).

• Identifying early indicators of emerging issues and assessing whether observed patterns represent meaningful safety concerns, helping prioritize and inform appropriate mitigations.

• Assessing the effectiveness of mitigations,such as product changes, safeguards, and guidance,using behavioral evidence and real-world outcomes.

• Contributing to incident reviews and post-incident analysis by bringing a behavioral perspective to root cause analysis and prevention.

• Bridging research and operations, translating academic and clinical literature into practical safeguards, policies, and product decisions.

You might thrive in this role if you:

• Bring 5+ years in forensic, clinical, trust and safety, or applied academic settings assessing risk of violence, self-harm, or addiction, with strong mixed-methods research skills.

• Have familiarity with AI systems, language models, or human-AI interaction dynamics, and are interested in applying psychological expertise to emerging AI risks (experience working on AI safety, trust & safety, or related domains is a plus).

• Can translate human behavior into structured intelligence, connecting individual cases to system-level patterns and risks.

• Are comfortable working across qualitative and quantitative inputs, including casework, interaction data, research literature, and metrics.

• Have experience designing or using risk frameworks, taxonomies, or evaluation methods to structure ambiguity.

• Communicate clearly across disciplines, turning complex behavioral insights into concise, actionable recommendations.

• Thrive in fast-moving, ambiguous environments, and can prioritize effectively under uncertainty.

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$230K – $342K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits connexion

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Role

We’re hiring a Data Scientist to support People Innovation Labs, a fast-moving engineering team embedded in the People organization focused on rethinking how we find and retain the best talent and empower everyone to do their best work.

From recruiting to culture, we’re designing systems and products that give our People Team a significant edge by infusing OpenAI’s models and first-principles thinking into every aspect of our work. Our projects range from greenfield 0-1 products like OpenHouse (our internal knowledge hub) to AI-powered automations and scalable recruiting tools. We’re defining the future of work at OpenAI, creating a blueprint for how AI can supercharge productivity, culture, and innovation.

In this role, you’ll be embedded with Product and Engineering to ensure our work drives measurable business outcomes.

What You’ll Do

• Define success metrics for agentic recruiting and HR systems, including leading indicators that enable weekly iteration.

• Design measurement and experimentation frameworks for always-on systems across every stage of the candidate and employee lifecycle , using holdouts, staged rollouts, and quasi-experimental methods when needed.

• Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value.

• Translate behavioral and model-driven signals into decisions: what to scale, where to intervene, and how to allocate human and compute attention across segments.

• Build repeatable decision loops (pre-launch criteria → post-launch read → next action) that convert analysis into shipped changes.

What We’re Looking For

• 10+ years in a quantitative role (e.g., Data Science, Decision Science), ideally at a product-led company or in the internal people software space.

• Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments.

• Strong technical fluency in SQL and Python, including working directly with messy, incomplete behavioral data to quantify impact.

• Proven track record of translating results into shipped decisions (product, lifecycle, targeting, routing).

• Strong business judgment and a bias toward action: able to scope ambiguous problems, define success, and move quickly from insight to strategy.

• Excellent communicator and partner to PMs/Engineers; comfortable influencing stakeholders and presenting recommendations to senior leadership.

• Interest in building company culture and/or recruiting the world’s most talented people.

Nice to Have

• Familiarity with large language models and AI-assisted operations platforms.

• Experience working on operational automation and decision systems (routing, prioritization, optimization).

• Experience operating in early-stage or rapidly evolving environments, including building measurement and experimentation frameworks from scratch.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

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Location: San Francisco

Department: Consumer Products

Job Type: Full time

Compensation

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Future of Computing Research team is an Applied Research team within the Consumer Devices group focused on developing new methods and models to support our vision as we advance forward in our mission of building AGI that benefits all of humanity.

About the Role

As a Research Engineer/Scientist on the Future of Computing Research team, you will work together with _both_ the best ML researchers in the world and the greatest design talent of our generation to push the frontier of model capabilities.

This role is based in San Francisco, CA. We follow a hybrid model with 4 days a week in the office and offer relocation assistance to new employees.

In this role you will:

• Train and evaluate SoTA models along axis that are important to our vision for future devices.

• Run through the necessary walls to take nascent research capabilities and turn them into capabilities we can build on top of.

• Help define how software works for decades to come.

You might thrive in this role if you:

• Have 5+ years of relevant work experience.

• Have a research background in utilizing and training language models to generate UI, and developing recipes to evaluate the quality / applicability of UI generated.

• Love exposure to a bit of everything – we’re collaborators with a hugely diverse set of research.

• Do rigorous science (rather than vibes based). We need confidence in the experiments we run to move quickly.

• Have already spent time in the weeds teaching models to speak and perceive.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

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This role is based in our San Francisco HQ. We offer relocation assistance to new employees.

In this role, you will:

• Lead our efforts in understanding and measuring the real-world safety impacts of OpenAI’s current and upcoming products
• Uncovering new ways to improve our approaches to measuring and mitigating harm and abuse
• Develop and implement statistical methods necessary to operationalize safety-related metrics
• Provide direction, guidance, and coordination of projects in the space
• Establish a data-driven culture within Safety Systems by driving the definition, tracking, and operationalizing of feature-, product-, and company-level metrics
• Create and disseminate dashboards, reports, and tools that enable the team and company to answer safety-related questions independently
• Develop safety data flywheel and provide safety research with production insights/data for training and evaluation.

You might thrive in this role if you have:

• 5+ years experience in a quantitative role navigating highly ambiguous environments, ideally as a founding data scientist or team lead at a hyper-growth product company or research org
• Proven leadership skills, including leading multiple data scientists and cross-functional teams
• Expertise in defining and implementing metrics, with a track record of operationalizing new feature and product-level metrics from scratch
• Excellent communication skills with demonstrated ability to communicate with product managers, engineers, and executives alike
• Strategic insights that extend beyond traditional statistical significance testing.

You could be an especially great fit if you have:

• Experience in trust and safety, integrity, anti-abuse, or related fields
• Demonstrated prior experience in NLP, large language models, or generative AI
• Strong statistical background, including knowledge of sampling, regression, causal analysis, and more

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Your primary responsibilities will include:

• Serving as the technical lead for designing and implementing GenAI solutions.
• Leading the entire lifecycle of AI projects, from translating high-level business needs into well-defined technical problems to deploying and maintaining enterprise-grade agentic solutions in our cloud environment.
• Partnering with IT, PRE, and business teams to ensure the seamless deployment, integration, and operational success of AI/ML solutions within the broader software architecture.
• Applying advanced analytical solutions (including machine learning and statistical models), and conducting analysis that consists of problem formulation, data extraction and pre-processing, modeling, validation, ongoing work, and presentations.
• Working with high-volume, high-dimensional structured and unstructured data from a variety of sources and implementing algorithms to identify anomalies, relationships, and trends.
• Developing complete system designs that include data, pre-processing, modeling, optimization, post-processing, and interfaces for user interaction.
• Interpreting and presenting complex modeling results, system designs, and strategic recommendations to technical peers, cross-functional teams, and senior leadership in a clear and compelling manner.

To succeed in this role, you will need:

• A Bachelor’s degree in Computer Science, Data Science, Statistics, Physics, Mathematics, or Engineering degree or a related field of study.
• 5+ years of professional experience in software engineering, machine learning, or a data-focused role.
• 3+ years of hands-on experience developing and deploying machine learning solutions using languages like Python, PySpark, or Scala.
• 1+ years of direct, hands-on experience building solutions with Large Language Models (LLMs), agentic frameworks (e.g., LangChain), or related Generative AI technologies.
• Proficiency with SQL and experience engineering data from diverse, high-volume sources.

Even better, you may have:

• An MS or PhD in a related field.
• Demonstrated success applying ML and AI to solve real-world industrial problems with large-scale data.
• Proficiency with version control (Git) and CI/CD practices within an agile development environment.
• Excellent programming skills, particularly interface development to generate data visualizations and to extract insights from large complex data sets.
• Strong interpersonal and leadership skills, with proven abilities to communicate complex topics to leaders and peers in a simple, clear, plan-oriented manner.
• A self-starter mentality, with the ability to navigate ambiguity and drive projects forward with minimal direction in a fast-paced environment.
• Ability to work in an environment where problems are not always well-defined.

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LOCATION: San Francisco DEPARTMENT: Data Science JOB TYPE: Full time WORK ARRANGEMENT: Hybrid

JOB DESCRIPTION:

Compensation

• $293K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Applied team safely brings OpenAI's technology to the world. We released ChatGPT; Plugins; DALL·E; and the APIs for GPT-4, GPT-3, embeddings, and fine-tuning. We also operate inference infrastructure at scale. There's a lot more on the immediate horizon.

Our customers build fast-growing businesses around our APIs, which power product features that were never before possible. ChatGPT is a prime example of what is currently possible. We simultaneously ensure that our powerful tools are used responsibly. Safe deployment is more important to us than unfettered growth.

The Scaled Abuse team works within our Applied Engineering organization identifying and responding to fraudsters on our platform. We are looking for a data scientist with anti fraud & abuse experience to help architect and build our next-generation anti-abuse systems.

About the Role

The Platform Abuse team protects OpenAI’s products from abuse. In the data scientist role, you will be responsible for discovering and mitigating new types of misuse, and scaling our detection techniques and processes. Platform Abuse is an especially exciting area since we believe most of the ways our technologies will be abused haven’t even been invented yet.

This role is based in our San Francisco HQ. We offer relocation assistance to new employees.

In this role, you will:

• Design and build systems for fraud detection and remediation while balancing fraud loss, cost of implementation, and customer experience.

• Work closely with finance, security, product, research, and trust & safety operations to holistically combat fraudulent and abusive actors on our system.

• Stay abreast of the latest techniques and tools to stay several steps ahead of determined and well resourced adversaries.

• Utilize GPT-5 and future models to more effectively combat fraud and abuse.

You might thrive in this role if you:

• Have experience on a highly technical trust and safety team and/or have worked closely with policy, content moderation, or security teams.

• Can use coding languages (Python preferred) to programmatically explore large datasets and generate actionable insights to solve problems.

• Proven ability to propose, design, and run rigorous experiments (A/B tests, quasi-experiments, simulations) with clear insights and actionable product recommendations, leveraging SQL and Python.

• Excellent communication skills with a track record of influencing cross-functional partners, including product managers, engineers, policy leads, and executives.

• Bonus if you have experience with deploying scaled detection solutions using large language models, embeddings, or fine tuning.

• 5+ years of quantitative experience in ambiguous environments, ideally as a data scientist at a hyper-growth company or research org, with exposure to fraud, abuse, or security problems.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through [this form](https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA). No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this [link](https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241).

[OpenAI Global Applicant Privacy Policy](https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf)

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Compensation Range: $293K - $325K

[

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A suitable candidate will have solid Software Engineering skills with practical experience in integrating machine learning models in production workflows in both online and offline settings. Moreover, the candidate will also have solid experience in building and optimizing distributed systems that are able to leverage available compute and storage resources to build efficient applications for serving users.

We hire people motivated to solve the hard problems, keen to work in a larger team of data scientists and engineers, and ready to make a difference in how search and AI assistant landscape evolves.

Responsibilities:

• Design, implement, and ship AI-first product capabilities end-to-end from rapid prototype to production, spanning LLM-powered services, retrieval/grounding pipelines, and intelligent UX experiences.

• Design and execute offline and online experiments to assess product impact, iterating on techniques to improve models and maximize impact.

• Own implementation across the full stack integrating front-end experiences, back-end services, and AI orchestration layers that connect models, context, and tools to deliver cohesive, extensible, high-performance systems.

• Collaborate with design, research, and platform teams to adapt or fine-tune, optimize LLMs/SLMs models for real-world customer scenarios, ensuring outcomes are contextual, transparent, and human-centered.

• Build agentic, tool-using, and multimodal workflows that reason across data and services; optimize for safety, latency, reliability, and cost efficiency.

• Contribute to engineering excellence secure-by-design, accessibility compliance, automated testing, and code craftsmanship across the product lifecycle.

• Drive live-site reliability and operational excellence, participating in On-Call rotations while maintaining a sustainable, high-ownership engineering culture.

Qualifications:

• Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

• Professional experience working with generative artificial intelligence, large language models or agent-based systems.

• Comfortable driving complex server and client architecture across large product teams.

• Hands-on experience with modern LLM evaluation techniques, including LLM-as-a-Judge, agentic evaluations and RAG assessments.

• A track record of delivering successful, large scale applied ML projects in an industry setting.

• Experience with MLOps practices, model versioning, automated testing, monitoring and CI/CD for machine learning.

• Cloud & Infrastructure: Skilled in building and operating infrastructure using Azure, AWS, or Google Cloud, and deploying containerized models with Docker, Kubernetes, or similar tools.

• Experience with proficient coding, debugging, and problem-solving skills.

• Excellent problem solving and data analysis skills.

• Outstanding communication and collaboration skills.

Preferred Qualifications:

• Master's Degree in Computer Science or related technical field with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

• OR Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

• OR equivalent experience.

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We are looking for a Staff MLE to join Surfaces Podcasts. The Surfaces Podcasts team builds the systems that power podcast recommendations across some of Spotify’s most visible experiences, including Home and the Now Playing view. We work across candidate generation, ranking, and embedding models to help listeners discover their favorite new podcast and engage deeply with their favorite shows.

We’re also shaping the next generation of personalization through transformer-based models that bring more dynamic, context-aware recommendations to millions of listeners. You’ll collaborate closely with teams across Personalization, Experience, and the Podcast Mission to evolve podcast listening across Spotify.

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development

• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.

• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

Requirements

• You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes.

• You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders.

• You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required.

• You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS.

• You care about agile software processes, data-driven development, reliability, and disciplined experimentation.

Where You’ll Be

• We offer you the flexibility to work where you work best! For this role, you can be within North America as long as we have a work location.

• This team operates within the Eastern Standard time zone for collaboration

Benefits

The United States base range for this position is $227,495- $324,993 equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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As a Senior Applied Scientist on Bing Places, you will work on challenging problems that require deep technical expertise and a solid focus on real-world impact. You will work end-to-end: from problem formulation and data analysis, through model development and experimentation, to production deployment and live flighting. You will collaborate closely with engineering and product partners to develop, experiment with, and ship models that operate at Microsoft scale, while contributing to the broader scientific community through publications and patents.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Formulate complex product and engineering problems as machine learning and AI tasks, and drive them from concept through production.

Design, implement, and evaluate ML- and LLM-based models that improve Bing Places quality, relevance, and coverage.

Conduct rigorous data analysis to understand system behavior, identify opportunities, and define success metrics.

Prototype new modeling approaches and iterate quickly based on offline evaluation and online experimentation.

Own experimentation pipelines, including offline validation and large-scale online A/B flighting.

Partner closely with engineers to integrate models into production systems and ensure long-term reliability and performance.

Drive technical direction within your problem space and influence broader modeling and platform decisions.

Document and communicate results through technical design reviews, papers, and patent filings.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)

OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

Preferred Qualifications:

Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

Solid foundation in machine learning, statistical methods, and data-driven problem solving.

Hands-on experience developing and evaluating models on large-scale, real-world datasets.

Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX, or similar).

Solid understanding of experimentation methodologies, including offline metrics and online A/B testing.

Ability to independently scope problems and deliver high-quality solutions in ambiguous environments.

Solid collaboration skills and experience working with engineering and product partners.

Ability to clearly communicate technical concepts and trade-offs to both technical and non-technical audiences.

4+ years of experience applying AI solutions or LLMs to real-world systems (RAG, ranking, classification, reasoning).

Background in search, information retrieval, knowledge graphs, or local/entity understanding.

Experience shipping models into large-scale production systems with real user impact.

Track record of publications or granted/pending patents.

Familiarity with distributed training, model optimization, and production ML infrastructure.

Comfort operating across the full lifecycle,from research and prototyping to production and live operations.

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Key responsibilities include owning and evolving our public REST API, building and maintaining client SDKs, shipping our Agent SDK, and treating documentation as a product. The successful candidate will collaborate with our DevRel, Product, and Platform teams to identify gaps in the developer ecosystem and run with them end to end.

We offer a competitive salary range of $215,000 to $285,000 plus benefits & equity, and a chance to work with a leading AI routing and infrastructure layer.

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Do you have a knack for imagining ways AI can help people feel more supported and productive? Do you thrive in a fast-paced environment, take the initiative to solve problems, and lead with positive energy, empathy, and kindness? We’d love you to consider joining us.

We’re a small, friendly team, who support each other to do our best work. We run lean, obsess about users, and always make our decisions based on the evidence. It’s a time of huge change in the AI landscape, and this role will put you right in the heart of it. You will impact the experience of hundreds of millions of users by creating experiences for the Copilot apps on PC, mobile, and Web, collaborating with internal and external partners to ship fast and innovate.

Our broader organization, Microsoft AI (MAI), is responsible for Copilot, Bing, Edge, and generative AI research. We combine world-class AI research together with top-notch design and product craft. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

As a Principal Product Manager, you’ll blend creativity, curiosity, and technical depth to help drive projects from concept to launch. You’ll partner closely with engineers, designers, other PMs, and AI researchers to turn ambiguous ideas into user-delighting experiences. You will:

Drive building world-class AI applications that will delight consumers and deliver business results.

Own a product area and be responsible for understanding user needs and behaviors, defining product requirements, managing end-to-end product development, launches and iterations.

Find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.

Translate business goals into product strategy, user experience, and technical requirements in collaboration with UX and AI model teams.

Define goals and performance indicators, set up and oversee experiments, measure success with data and research.

Amplify your impact by using AI to research, prototype, and dive into data to understand user behavior, product performance, and new opportunities.

Communicate clearly and collaborate effectively across cross-functional teams.

Embody our Culture and Values.

Qualifications:

Required Qualifications:

Bachelor’s Degree AND 8+ years experience in product/service/program management or software development OR equivalent experience.

Preferred Qualifications:

Bachelor’s Degree AND 10+ years experience in product/service/program management or software development OR equivalent experience.

2+ years experience building consumer products, including personal productivity, search, education, or messaging.

Experience using AI tools for product research, planning, and AI-assisted coding tasks.

Experience building 0 to 1 products and driving or contributing to product design / prototyping.

Experience working with Machine Learning or Large Language Models.

Ability to operate in a fast-paced environment, manage multiple priorities, and adapt to changing requirements.

Demonstrated ability to collaborate effectively and contribute to an inclusive, growth-oriented team culture.

Product Management IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.

Product Management IC6 – The typical base pay range for this role across the U.S. is USD $163,000 – $296,400 per year.

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We are looking for a Machine Learning Engineer to join the Personalization team. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

Requirements

• Strong background in machine learning, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes
• Hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required
• Some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
• Care about agile software processes, data-driven development, reliability, and disciplined experimentation

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have a work location
• This team operates within the Eastern Standard time zone for collaboration

Additional Information

The United States base range for this position is $227,495-$324,993 equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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We are looking for a Machine Learning Engineer to join the Personalization team. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

Requirements

• Strong background in machine learning, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes
• Hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required
• Some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
• Care about agile software processes, data-driven development, reliability, and disciplined experimentation

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have a work location
• This team operates within the Eastern Standard time zone for collaboration

Additional Information

The United States base range for this position is $227,495- $324,993 equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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Responsibilities

• Design and ship production-grade machine learning systems powering conversational and agentic AI experiences
• Build systems that interpret user intent, manage context across multi-turn interactions, and handle ambiguity reliably at scale
• Develop and evolve agentic workflows including memory, context management, and multi-step tool orchestration
• Create evaluation frameworks, including LLM-as-judge pipelines, to measure quality and guide iteration
• Partner closely with product, engineering, and design to deliver seamless, user-facing experiences
• Balance experimentation with production rigor, ensuring performance, latency, and reliability at Spotify scale
• Continuously improve agent behavior through tight feedback loops between evaluation and real-world usage

Requirements

• 5+ years of experience building and shipping machine learning systems in production environments
• Experienced with large language models and have worked on real-world applications beyond experimentation; shipped and maintained large scale systems with LLMs
• Deep understanding of challenges in conversational or agentic systems, such as context handling and multi-step reasoning
• Know how to evaluate ML systems rigorously and have experience designing metrics or evaluation pipelines
• Comfortable debugging complex interactions between models, tools, and system constraints like latency
• Care about building reliable, scalable systems that deliver high-quality user experiences
• Enjoy working cross-functionally and contributing to a collaborative, inclusive team environment

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We’re looking for a hands-on ML engineer to prototype and productionize complex classification flows on real production logs, operate prompted classifiers at scale (ad hoc and scheduled), and build secure, compliant data-labeling pipelines.

We’re looking for someone with experience in data pipelines, data science, and machine learning, as well as a strong communicator and great teammate.

The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.

Generalize machine learning (ML) solutions into repeatable frameworks.

Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.

Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.

Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.

Independently write efficient, readable, extensible code and model pipelines.

Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications:

7+ years’ experience writing production-quality Python or Java or Scala code.

5+ years’ experience in distributed systems design and implementation of large scale data processing systems

3+ years’ experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph

Demonstrated interest in Responsible AI.

Experience prompting, evaluating, and working with large language models.

#MicrosoftAI #mai-datainsights #mai-datainsights

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As a Senior Applied Data Scientist, you will use Large Language Models (LLMs), Small Language Models (SLMs), and Retrieval-Augmented Generation (RAG) models to create modern, scalable, and efficient systems. You will collaborate with a dynamic, research-driven team that values curiosity, creativity, and a growth mindset.

In this role, you will design and implement advanced AI solutions, staying at the forefront of the latest research in machine learning and natural language processing. You’ll have the opportunity to accelerate your career by working with industry leaders, refining your expertise in applied sciences, and contributing to impactful, customer-centric projects.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

• Develop and deploy solutions leveraging LLMs, SLMs, and RAG models to solve complex data challenges and enhance the Bing Places platform.
• Stay informed about the latest advancements in AI/ML research, evaluate their applicability, and integrate them into scalable systems.
• Collaborate across multidisciplinary teams, including engineers, data scientists, and product managers, to translate research insights into actionable solutions.
• Create robust pipelines for data processing, training, and inferencing to optimize model performance and scalability.
• Mentor junior team members, foster knowledge sharing, and support recruiting efforts to build a world-class research team.
• Uphold ethical principles in AI, ensuring solutions are fair, unbiased, and aligned with Microsoft’s privacy standards.

Qualifications:

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 3+ years of experience in publishing peer-reviewed research or presenting at conferences.
• Proven expertise in developing and deploying AI solutions in production environments.
• Experience working with cloud-based infrastructure and big data technologies.

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The future of smart mobility hinges on the intelligent application of data, metrics, and analytics. As part of our Global Data Insight & Analytics (GDI&A) team, you'll play a pivotal role. We serve as Ford's trusted advisers, providing clear insights into business conditions, customer needs, and the competitive environment. Our work empowers key decision-makers to act decisively and positively. By leveraging your expertise in data and analytics, you can contribute to timely, evidence-based decision-making.

Ford's GDI&A department is on the hunt for talented individuals skilled in Machine Learning, Big Data, Statistics, Econometrics, and Optimization. Our mission is to foster evidence-based decisions by unlocking insights from data. Our projects span various domains, including Connected Vehicle, Smart Mobility, Operations, Manufacturing, Supply Chain, Logistics, and Warranty Analytics.

We're looking for exceptional Machine Learning and AI scientists who are eager to engage in all project stages, from problem identification to model deployment. Ideal candidates are self-motivated, have a strong sense of initiative, and a lifelong passion for learning to navigate the rapidly evolving field of mobility technology. We offer the freedom to conduct original research, select the most suitable methodologies, and tackle world-class machine learning challenges.

This role involves applying AI and ML to innovate in areas such as autonomous vehicles, cybersecurity, customer interaction, and product design. You'll be part of a forward-thinking team dedicated to using AI and ML to create groundbreaking solutions and shape our strategic direction in these fields and beyond.

Responsibilities:

• Analyze source data and data flows, working with structured and unstructured data (text, audio, images, video, etc.)

• Manipulate high-volume, high-dimensionality data from varying sources to expose and highlight patterns, anomalies, relationships, and trends

• Apply AI and Machine Learning technology to solve complex, real-world problems

• Analyze and visualize diverse sources of data, interpret results in a business context and report results clearly and concisely

• Fulfill problem formulation and ML technique consulting requests in a timely manner

• Communicate and present analytical models to business customers and executive management

• Work collaboratively with different business partners and be able to present results in a clear and concise manner

Qualifications:

• Bachelor’s degree in computer science, mathematics, statistics, operations research, or related field

• Experience with the complete software lifecycle

• 1+ years of experience with delivering and maintaining production software products

• Strong technical writing and oral communication skills

• Expertise in one or more core domains involved in machine learning model deployment, including data engineering, model building, MLOps

• Experience in productionizing generative AI to solve critical business problems

Preferred Qualifications:

• Doctorate in computer science, mathematics, statistics, operations research, or related field AND 1+ year(s) data-science experience (e.g. managing structured and unstructured data, applying statistical techniques and reporting results)

• OR Master’s degree in computer science, mathematics, statistics, operations research, or related field AND 3+ year(s) data-science

• OR bachelor’s degree in computer science, mathematics, statistics, operations research, or related field AND 5+ year(s) data-science experience

• Experience with cloud-based deployments and best practices

• Demonstrated contributions and expertise in one or more of the following AI domains:

• Natural Language Processing (finetuning and distillation of LLMs, evaluation of LLM-powered applications, deploying models at scale, including development and deployment of LLM-driven AI agents capable of autonomous task execution and tool integration)

• Computer Vision (anomaly detection / few-shot learning, multi-sensor fusion, object detection and tracking, scene segmentation, motion planning and prediction)

• Generative AI (fine tuning stable diffusion models, multi-modal large language models, generation in the 3D domain)

• Physics-informed neural networks (ML for computational fluid dynamics or finite element analysis, point cloud or mesh-based neural networks, PDE surrogate modelling)

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We are a dynamic team of engineers and applied scientists, pushing the boundaries of Generative AI to deliver cutting-edge tools that drive impact at scale. We are looking for a Principal Applied Scientist to lead the development of the Copilot Chat Assistant to help advertisers navigate every step of their journey.

You will design and productionize systems that orchestrate multiple Large Language Models (LLMs) using Agentic Frameworks (e.g., Semantic Kernel) and leverages Vector Databases, Retrieval Augmented Generation (RAG), and Model Context Protocol (MCP) based tools to solve complex, multi-step tasks. Your work will directly impact the topline revenue metrics for Microsoft Advertising.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

Own the science roadmap for Copilot Chat Assistant – from ideation to reliable, safe production launches.

Design multi-agent reasoning systems that blend LLM orchestration, tool use, and state management for long-running tasks.

Build robust retrieval pipelines (RAG + vector stores) for precise, grounded answers across advertiser and campaign domains.

Advance prompt/program design (planning, decomposition, self-reflection, evaluation) to boost accuracy, latency, and cost efficiency.

Ship real features with engineering partners: telemetry, online evaluations/A-B tests, guardrails, red-teaming, and quality bars.

Mentor and multiply: guide applied scientists and engineers, set best practices, and raise the bar for scientific rigor.

Measure impact: define success metrics, instrument experiments, and iterate quickly based on data and customer feedback.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

Preferred Qualifications:

Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

5+ years experience developing and deploying live production systems, as part of a product team.

7+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Experience using LLMs, agentic frameworks, RAG, vector databases to build complex production systems.

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We’re looking for a Principal Product Manager who sees that opportunity clearly and has the conviction and the hands to go build it. Someone who invents on behalf of customers rather than waiting to be told what they want. Someone who thinks about AI not just as a set of features to add, but as the means by which we rethink the whole product on behalf of the people who use it every day. The role is yours to define. The surface area is massive. The moment is now.

As a Principal Product Manager, you will own the AI-native product strategy for Microsoft Edge, defining how AI collapses the distance between what people want and what they can accomplish , across research, tasks, decisions, and everything else people bring to their browser. You will invent on behalf of billions of users: form solid, well-reasoned convictions about what people need before they can articulate it themselves, and build toward that vision.

Turn frontier AI capabilities into experiences customers actually want to use, not demos that impress in a conference room and disappear six months later. Run fast, hypothesis-driven experimentation cycles across new AI interaction patterns and UI flows; let real usage data sharpen your convictions. Partner with research teams to identify model advances with real customer value and move them from research to shipped product. Work with data science and growth teams to ensure AI-powered features reach and retain the users who need them most.

Set clear direction in ambiguous spaces, make tradeoffs visible to leadership, and drive alignment across partner teams. Operate as a senior IC: coach PMs on adjacent workstreams, unblock critical path items, and hold a high bar for what “done” means.

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As a full stack engineer, you’ll have the unique opportunity to work across the entire technology stack, from high-performance backend services to stunning user interfaces. As a Full Stack Software Engineer on this team, you will work across both backend and frontend systems to deliver end-to-end features for Copilot and Bing. On the backend, you’ll work on building modern C#-based service layers, high-performance distributed systems and prompt engineering. On the frontend, you’ll build beautiful, responsive experiences using server-side and client-side frameworks.

This opportunity will allow you to accelerate your career as a versatile engineer, develop deep expertise across the full technology stack, and gain hands-on experience shipping AI-powered features to billions of users.

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As a full stack engineer, you’ll have the unique opportunity to work across the entire technology stack, from high-performance backend services to stunning user interfaces. As a Full Stack Software Engineer on this team, you will work across both backend and frontend systems to deliver end-to-end features for Copilot and Bing. On the backend, you’ll work on building modern C#-based service layers, high-performance distributed systems and prompt engineering. On the frontend, you’ll build beautiful, responsive experiences using server-side and client-side frameworks.

This opportunity will allow you to accelerate your career as a versatile engineer, develop deep expertise across the full technology stack, and gain hands-on experience shipping AI-powered features to billions of users.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

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As a full stack engineer, you’ll have the unique opportunity to work across the entire technology stack, from high-performance backend services to stunning user interfaces. As a Full Stack Software Engineer II on this team, you will work across both backend and frontend systems to deliver end-to-end features for Copilot and Bing. On the backend, you’ll work on building modern C#-based service layers, high-performance distributed systems and prompt engineering. On the frontend, you’ll build beautiful, responsive experiences using server-side and client-side frameworks.

This opportunity will allow you to accelerate your career as a versatile engineer, develop deep expertise across the full technology stack, and gain hands-on experience shipping AI-powered features to billions of users.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

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As a full stack engineer, you’ll have the unique opportunity to work across the entire technology stack, from high-performance backend services to stunning user interfaces. As a Full Stack Software Engineer II on this team, you will work across both backend and frontend systems to deliver end-to-end features for Copilot and Bing.

On the backend, you’ll work on building modern C#-based service layers, high-performance distributed systems and prompt engineering. On the frontend, you’ll build beautiful, responsive experiences using server-side and client-side frameworks.

This opportunity will allow you to accelerate your career as a versatile engineer, develop deep expertise across the full technology stack, and gain hands-on experience shipping AI-powered features to billions of users.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

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You'll join a team building shared web tooling, frameworks, and operator-facing products that power Spotify's internal financial workflows. Our mission is simple (but not easy): make complex work easier, smoother, and more intuitive for our operators, while keeping our infrastructure scalable and easy to build on.

Responsibilities

• Contribute to the product vision and roadmap for shared web tooling and operator-facing products used by finance teams
• Support and evolve a portfolio of internal platforms and workflow systems, from established tools to new capabilities
• Work closely with R&D and end users to understand needs, validate problems, and deliver meaningful solutions
• Lead product discovery for new features and improvements, focusing on usability, clarity, and operator efficiency
• Help shape AI-assisted capabilities within workflows, ensuring they are useful, transparent, and trustworthy
• Partner with engineers to define shared web frameworks and components that enable other teams to build efficiently while maintaining UX consistency
• Balance short-term delivery with long-term platform thinking, including scalability, reliability, and maintainability
• Communicate clearly with stakeholders to align on priorities, trade-offs, and outcomes
• Collaborate closely with engineering and design to support a healthy, high-performing team environment

Requirements

• You enjoy collaborating with cross-functional partners and driving outcomes through strong relationships
• You are experienced with, or curious about, building operator-facing or workflow-driven products and simplifying complex systems
• You are comfortable working closely with engineers and can engage in technical discussions around system design and architecture
• You have a solid understanding of modern AI/ML concepts such as large language models, prompt design, evaluation, and their trade-offs
• You understand how AI can support human decision-making while maintaining transparency and trust
• You learn quickly and synthesize inputs from users, data, and technical contexts to inform decisions
• You communicate clearly and adapt your storytelling to different audiences
• You care deeply about user-centered, data-informed product development
• You are interested in platforms and shared tooling, and how they enable teams while maintaining a strong user experience
• You have experience with internal tools, financial systems, or workflow-based products, or you’re excited to learn in this space
• You are comfortable navigating ambiguity and influencing decisions through insight

Benefits

• Flexible working hours and remote work options
• Access to cutting-edge technology and tools
• Opportunities for professional growth and development
• Collaborative and dynamic work environment

How to Apply

If you're passionate about building innovative products and driving business outcomes, we'd love to hear from you. Please submit your application, including your resume and a cover letter, to [insert contact email or link].

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Users turn to Copilot for support in all types of endeavors, making it critical that we ensure our AI systems behave safely and align with organisational values.

You may be responsible for developing new methods to evaluate LLMs, experimenting with data collection techniques, implementing safety orchestration methods and mitigations, and training content classifiers to support the Copilot experience.

We’re looking for outstanding individuals with experience in machine learning or machine learning infrastructure who are also strong communicators and great teammates. The right candidate takes the initiative and enjoys building world-class, trustworthy AI experiences and products in a fast-paced environment.

Microsoft’s mission is to empower every person and every organisation on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realise our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

This expectation is subject to local law and may vary by jurisdiction.

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As a Principal Software Engineer, you will provide technical leadership across the full Mico engineering stack. You will drive architecture decisions, set the engineering quality bar, and lead a globally distributed team spanning the US, Japan, and China in building and scaling one of Microsoft’s most visible consumer AI experiences. You will be responsible for the Picasso rendering engine, cross-platform animation delivery, LLM orchestration, long-term memory systems, and the adaptive personalisation framework that makes Mico feel alive.

Microsoft’s mission is to empower every person and every organisation on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realise our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

Own the end-to-end system architecture for the Mico experience, spanning 3D rendering (Picasso stack / Babylon Native), real-time character animation, lip-sync, locomotion, and expression systems.

Design and evolve the LLM orchestration layer that powers Mico’s conversational intelligence, including prompt management, skill routing, and context window optimisation.

Architect the long-term memory system (Cosmos DB) , defining schemas, event handling, message partitioning, and retrieval strategies that enable Mico to remember and adapt to each user.

Drive the adaptive personalisation framework that governs how Mico adjusts behaviour, tone, and interaction patterns based on user history and preferences.

Architect solutions that work consistently across CMC (web), iOS, Android, and Windows surfaces, defining shared rendering abstractions and platform-specific optimisations.

Coordinate Unified UI integration , ensuring Mico’s visual presence (header, footer, composer, snap-to positioning) works seamlessly with the Copilot shell across all form factors.

Own the asset pipeline: CDN-delivered 3D models, video backgrounds, animation configs, and per-surface configuration files (e.g., mico_config_cmc.json, mobile configs).

Performance, Reliability, and Observability: Design systems for millions of daily active users , low-latency rendering, efficient asset delivery via CDN, graceful degradation on low-end devices, and robust error recovery.

Build and maintain observability dashboards using Azure Data Explorer (Kusto) to monitor Mico engagement, conversation duration, animation frame rates, and error rates across surfaces.

Partner with data science to design controlled flights and ablation experiments, interpret telemetry, and use data to guide decisions.

Qualifications:

Required Qualifications:

Bachelor’s degree in computer science or a related technical discipline, along with at least 8 years of engineering experience, including proficiency in programming languages such as C++, C#, Java, JavaScript, or Python.

Extensive expertise in iOS and/or Android development; strong full-stack engineering capabilities with comprehensive knowledge of consumer product development.

Demonstrated ability to lead through influence, establishing technical direction for teams of 3–10 engineers across US, Japan, and China time zones.

Experienced in conducting architecture reviews, design sprints, and technical alignment sessions.

Solid background in AI, including hands-on experience developing AI products or integrating AI into daily development processes.

Speaking English is required for daily work.

Preferred Qualifications:

Ability to establish automated evaluation pipelines for 3D animation fidelity, lip-sync accuracy, expression transitions, and locomotion smoothness , a frontier challenge unique to character AI.

Ability to drive mobile E2E test coverage and improve staging environment fidelity, so issues are caught before production.

Ability to define and enforce quality gates in CI/CD pipelines, ensuring regressions in animation rendering, voice interaction, and Mico Moments content delivery are caught automatically.

Ability to own the security posture for Mico systems , S360 compliance, resource ownership audits, and data classification for user memory and personalisation data.

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We are a dynamic team of engineers and applied scientists, pushing the boundaries of Generative AI to deliver cutting-edge tools that drive impact at scale. We are looking for a Principal Applied Scientist to lead the development of the Copilot Chat Assistant to help advertisers navigate every step of their journey.

You will design and productionize systems that orchestrate multiple Large Language Models (LLMs) using Agentic Frameworks (e.g., Semantic Kernel) and leverages Vector Databases, Retrieval Augmented Generation (RAG), and Model Context Protocol (MCP) based tools to solve complex, multi-step tasks. Your work will directly impact the topline revenue metrics for Microsoft Advertising.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

Own the science roadmap for Copilot Chat Assistant – from ideation to reliable, safe production launches.

Design multi-agent reasoning systems that blend LLM orchestration, tool use, and state management for long-running tasks.

Build robust retrieval pipelines (RAG + vector stores) for precise, grounded answers across advertiser and campaign domains.

Advance prompt/program design (planning, decomposition, self-reflection, evaluation) to boost accuracy, latency, and cost efficiency.

Ship real features with engineering partners: telemetry, online evaluations/A-B tests, guardrails, red-teaming, and quality bars.

Mentor and multiply: guide applied scientists and engineers, set best practices, and raise the bar for scientific rigor.

Measure impact: define success metrics, instrument experiments, and iterate quickly based on data and customer feedback.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

Preferred Qualifications:

Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

OR equivalent experience.

5+ years experience developing and deploying live production systems, as part of a product team.

7+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Experience using LLMs, agentic frameworks, RAG, vector databases to build complex production systems.

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We're looking for a hands-on ML engineer to prototype and productionize complex classification flows on real production logs, operate prompted classifiers at scale (ad hoc and scheduled), and build secure, compliant data-labeling pipelines.

We're looking for someone with experience in data pipelines, data science, and machine learning, as well as a strong communicator and great teammate.

The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment.

Microsoft's mission is to empower every person and every organization on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.

Generalize machine learning (ML) solutions into repeatable frameworks.

Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.

Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.

Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.

Independently write efficient, readable, extensible code and model pipelines.

Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications:

7+ years’ experience writing production-quality Python or Java or Scala code.

5+ years’ experience in distributed systems design and implementation of large scale data processing systems

3+ years’ experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph

Demonstrated interest in Responsible AI.

Experience prompting, evaluating, and working with large language models.

#MicrosoftAI #mai-datainsights #mai-datainsights

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We’re looking for a hands-on ML engineer to prototype and productionize complex classification flows on real production logs, operate prompted classifiers at scale (ad hoc and scheduled), and build secure, compliant data-labeling pipelines.

We’re looking for someone with experience in data pipelines, data science, and machine learning, as well as a strong communicator and great teammate.

The right candidate takes the initiative and enjoys building world-class consumer experiences and products in a fast-paced environment.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.

Generalize machine learning (ML) solutions into repeatable frameworks.

Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.

Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.

Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.

Independently write efficient, readable, extensible code and model pipelines.

Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor.

Qualifications:

Required Qualifications:

Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications:

7+ years’ experience writing production-quality Python or Java or Scala code.

5+ years’ experience in distributed systems design and implementation of large scale data processing systems

3+ years’ experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph

Demonstrated interest in Responsible AI.

Experience prompting, evaluating, and working with large language models.

#MicrosoftAI #mai-datainsights #mai-datainsights

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In this role, you'll help turn raw model capability into reliable, aligned, and measurable performance improvements, directly shaping how frontier models behave in real-world deployments.

About the Role:

Microsoft AI is building the next generation of frontier models that power Copilot and other large-scale AI experiences. The Post-Training team is responsible for transforming powerful pretrained models into robust, aligned, and high-performing systems used by millions of people worldwide.

Our work focuses on improving general quality, instruction following, coding and math ability, tool use, agentic behaviors, personality, and other critical model capabilities. We operate across the full post-training lifecycle , from data generation and curation, to evaluation and diagnostics, to reward modeling and reinforcement learning.

We are a small, highly autonomous team that works closely with pre-training, product, and engineering partners to rapidly iterate on ideas, run large-scale experiments, and safely advance model capabilities. Each team member owns meaningful parts of the post-training pipeline and has direct access to the compute, data, and decision-making needed to move quickly from insight to production.

Microsoft Superintelligence Team

This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence,ultra-capable systems that remain controllable, safety-aligned, and anchored to human values.

Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society,advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact.

Responsibilities

Design evaluations of advanced model capabilities and use them to drive rapid, high-signal iteration loops Work with vendors to produce high quality evaluation and training data Build data pipelines to produce high quality evaluation and training data Build data flywheels to hill-climb on model weaknesses, using data from various surfaces where our models are deployed Ensure optimal quality, quantity and coverage of data across our post-training stages Run post-training experiments and ablations to produce models that climb our evals Embody our culture and values.

We’re Looking For People Who:

Have deep experience with LLMs, either training them or applying them in production Have developed production-scale data pipelines for synthesizing, curating, or processing large quantities of data Can design, run, and interpret large-scale ML experiments with careful statistical and empirical reasoning.

Possess strong generalist engineering and mathematical skills.

Have clear written and verbal communication, and the ability to collaborate effectively with researchers, engineers and other disciplines.

Bonus skills:

Demonstrated SOTA results in any area of large-scale training, inference, or evaluation.

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At Coinbase, our mission is to increase economic freedom in the world.

We’re seeking a very specific candidate who is passionate about our mission and believes in the power of crypto and blockchain technology to update the financial system.

This role will guide a team working across a hybrid ecosystem of internal LLM-driven agents and key third-party integrations.

Responsibilities:

Lead AI Strategy & Execution: Drive the roadmap for our conversational AI stack, moving beyond simple decision trees into LLM-driven reasoning, RAG, and agentic workflows.

Orchestrate the AI Ecosystem: Oversee the integration of third-party AI solutions while simultaneously scaling our in-house LLM infrastructure to handle high-stakes crypto support queries.

Build Evaluation & Guardrails: Establish rigorous AI evaluation frameworks (LLM-as-a-judge) and feedback loops to ensure our models are accurate, grounded, and compliant with global financial regulations.

Agentic Automation: Move from "chat" to "action" by building secure pathways for AI agents to perform complex tasks (e.g., transaction troubleshooting, account recovery) via internal APIs.

Drive Technical Architecture: Define how we handle vector databases, prompt engineering, and context window management to provide a personalised experience for every Coinbase user.

Operational Excellence: Own the reliability of AI services, including latency optimisation, cost management (token usage), and fallback mechanisms to human agents.

Requirements:

8+ years of software engineering experience, with 2+ years leading high-performing teams in a fast-paced environment.

Hands-on AI/ML Leadership: Proven experience shipping products powered by Large Language Models (LLMs).

Systems Thinking: Experience building RAG (Retrieval-Augmented Generation) pipelines and managing the data lifecycle required to ground AI in real-time knowledge.

Platform Mindset: You’ve built scalable, distributed systems and understand how to integrate AI components into a high-traffic production environment (Go, Ruby, or similar).

Evaluation Obsessed: You don’t just "vibe check" AI; you have experience with quantitative evaluation frameworks to measure hallucination rates, accuracy, and customer sentiment.

Security & Safety First: A deep understanding of how to build AI "guardrails",ensuring models don’t leak PII or hallucinate financial advice.

Nice to haves:

Experience with Vector Databases (e.g., Pinecone, Weaviate, Milvus) and AI Orchestration frameworks (e.g., LangChain, LlamaIndex).

Experience in FinTech or Crypto, specifically navigating the balance between AI innovation and strict regulatory/compliance requirements.

Background in NLP (Natural Language Processing) or traditional Machine Learning before the Generative AI boom.

Proficiency in Golang and experience with modern cloud-native infrastructure (AWS, Kubernetes).

Pay Transparency Notice: The target annual base salary for this position can range as detailed below.

Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, and vision).

Annual base salary range (excluding equity and bonus):

₹9,424,500-₹9,424,500 INR

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This role requires deep fluency in how large language models work, hands-on experience with AI agent architectures, and the ability to partner closely with Engineering on production systems.

As an AI Agent Architect, you will:

Own Agent retrieval accuracy and relevance. Architect the knowledge systems that enable AI agents to surface the right answer on the first try. Measure and improve retrieval precision, contextual relevance, and hallucination rates.

Drive automated resolution rates. Build the decision frameworks that allow agents to take confident actions. What APIs do agents need to access? When can they make account modifications? You're accountable for encoding business logic into auditable, predictable systems that resolve issues without human intervention.

Manage AI safety and trust. Establish the guardrails that keep resolution rates high while failure rates stay low. You're responsible for what the agent doesn't do wrong: edge cases caught, prompt injection blocked, unintended behaviours prevented.

Own the feedback loop. Monitor the observability layer that turns agent behaviour into actionable insights. Instrument retrieval accuracy, action success rates, and failure patterns. Use this data to drive measurable week-over-week improvements in agent performance.

Continuously improve agent quality. Develop and maintain the prompt architecture that governs how agents reason and respond. Build systematic approaches to versioning, A/B testing, and performance evaluation, measuring consistency, accuracy, and adaptability across scenarios.

Drive integration strategy. Architect how agents connect to external systems,billing platforms, CRMs, internal tools, Airtable APIs. Define authentication patterns, error handling, and data transformation. Uptime, error rates, and data accuracy are your metrics.

You are:

Familiar with concepts like RAG architectures, prompt engineering patterns, chain-of-thought reasoning, and agent frameworks. You've built or significantly contributed to AI-powered systems in production.

Able to think in terms of data flows, state management, error handling, and edge cases. You can design complex systems that are both powerful and reliable. You've likely worked in roles like solutions architecture, platform engineering, or technical program management.

Able to write scripts, work with APIs, query databases, and prototype solutions. You're not a full-time software engineer, but you're dangerous enough to build, test, and validate technical approaches independently.

Able to instrument systems, analyse logs, and use data to diagnose issues and validate improvements. You build dashboards, define metrics, and can tie technical changes to business outcomes like resolution rates and customer satisfaction.

Able to explain complex AI system behaviour to non-technical stakeholders, write clear technical documentation, and translate business requirements into system specifications. You're effective working across engineering, product, and operations teams.

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The Economic Research team at Anthropic studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data that is clear-eyed about the economic effects of AI to help policymakers, businesses, and the public understand and navigate the transition to powerful AI.

In this role, you will work closely with teams across Anthropic,including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy,to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting & implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating ambiguity, and demonstrating an eagerness to develop their own research & technical skills.

Responsibilities:

• Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.
• Expand privacy-preserving tools to enable new analytic functionality to support research needs.
• Design and implement novel data systems leveraging language models (e.g., CLIO) where traditional software engineering patterns don't yet exist.
• Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis.
• Contribute to the strategic development of the economic research data foundations roadmap
• Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure
• Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
• Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards.
• Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission

You might be a good fit if you have:

• Experience working with Research Scientists and Economists on ambiguous AI and economic projects
• Experience with building and maintaining data infrastructure, large datasets, and internal tools in production environments.
• Experience with cloud infrastructure platforms such as AWS or GCP.
• Take pride in writing clean, well-documented code in Python that others can build upon
• Are comfortable making technical decisions with incomplete information while maintaining high engineering standards
• Are comfortable getting up-to-speed quickly on unfamiliar codebases, and can work well with other engineers with different backgrounds across the organization
• Have a track record of using technical infrastructure to interface effectively with machine learning models
• Have experience deriving insights from imperfect data streams
• Have experience building systems and products on top of LLMs
• Have experience incubating and maturing tooling platforms used by a wide variety of stakeholders
• A passion for Anthropic's mission of building helpful, honest, and harmless AI and understanding its economic implications.
• A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description.
• Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise.

Strong candidates may have:

• Background in econometrics, statistics, or quantitative social science research
• Experience building data infrastructure and data foundations for research
• Familiarity with large language models, AI systems, or ML research workflows
• Prior work on projects related to labor economics, technology adoption, or economic measurement

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The program is designed to foster AI research and engineering talent. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on their projects. In one of our earlier cohorts, over 80% of fellows produced papers.

We run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond.

To be eligible, you must be fluent in Python programming, available to work full-time on the Fellows program, and have a strong technical background in computer science, mathematics, or physics.

The program is open to individuals from underrepresented groups and encourages applicants to apply even if they don't meet every qualification.

Strong candidates may also have a strong background in a discipline relevant to a specific Fellows workstream (e.g. economics, social sciences, or cybersecurity) and experience in areas of research or engineering related to their workstream.

ML Systems & Performance Fellows will work on projects such as building a CPU simulator for accelerator workloads, adding backends for different accelerators on an open source project, building on demand infrastructure for other infrastructure heavy fellows projects, and building complex synthetic data or environment pipelines.

The program is a great opportunity for individuals who are motivated by making sure AI is safe and beneficial for society as a whole, excited to transition into empirical AI research, and thrive in fast-paced, collaborative environments.

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We're hiring an experienced Staff AI Product Designer to play a critical role in driving the technical and product vision for priority users of Gemini, with a specific focus on small businesses. You'll be responsible for defining the product, model, and overall experiences from the ground up. You will architect intuitive interfaces, create robust design systems, and develop business strategies and communication frameworks to ensure high-quality app behavior and model responses tailored to the needs of small business owners.

As a key technical and design leader, you will work hand-in-hand with engineers, research scientists, and product managers to build transformative 0-to-1 solutions. You will play a crucial role in shaping how Small Businesses communicate, operate, and scale using generative AI.

Key Responsibilities:

• Drive big product vision & build from the ground up: Lead the technical and design vision for Gemini’s Small Business experiences, designing and building foundational, 0-to-1 features and capabilities entirely from scratch.

• Define business & response strategy: Develop business strategies, communication frameworks, system instructions, and rubrics specifically designed to improve model response quality and relevance for use cases.

• Architect intuitive AI experiences: Lead the design of user interfaces and consumer experiences for Gemini across diverse platforms, ensuring natural, efficient, and deeply helpful multimodal interactions.

• Shape LLM behavior: Partner closely with Product, Engineering, and other teams to define user-centered quality standards, establish evaluation methods, and directly shape the behavior and responses of underlying LLM models.

• Design with a systems-thinking approach: Apply a macro-level systems-thinking approach to design, understanding the impact of individual components on the overall AI-powered experience, optimized for AI's probabilistic nature.

• Craft visually compelling & fluid interfaces: Create high-fidelity visual designs, layouts, and engaging motion/interaction designs that make complex, AI-driven information highly accessible and aesthetically pleasing.

• Bridge design and technology: Leverage deep technical understanding to write system instructions and utilize diverse prototyping methods, simulating model responses to inform design requirements and engineering approaches.

• Champion innovation & ethical design: Stay at the forefront of generative AI advancements, considering the intricate interplay of AI models, UI, user safety, and societal impact to develop responsible, user-centered solutions.

Minimum Qualifications:

• 10+ years of experience designing user interfaces and shaping content/responses for complex software applications, with specific experience designing for conversational AI / LLMs.

• A robust portfolio showcasing fluency in visual, motion, and interaction design, alongside examples of content/product strategy and 0-to-1 AI-powered product builds.

• Experience designing tools, communication platforms, or product experiences specifically tailored for Small and Medium Businesses (SMBs).

• Demonstrated expertise in driving big product vision and guiding cross-functional teams (Engineering, PM, Research) through ideation, validation, and iteration.

• Strong systems thinker capable of moving seamlessly between deep technical model evaluation and high-level UX/UI design.

• Demonstrated experience thriving in a startup environment, comfortable with the ambiguity, rapid iteration, and resourcefulness required to build from the ground up.

• Excellent communication and storytelling capabilities, with the ability to articulate complex technical and design positions to leadership and cross-company teams.

Preferred Qualifications:

• Experience training, tuning, and evaluating large language models or other generative AI technologies.

• Proven experience in shepherding radical ideas that leverage AI to create truly transformative product experiences.

• Experience gathering and working with data to identify loss patterns in model output and create actionable insights.

• Experience prototyping interactive experiences using tools like Figma, ProtoPie, or Framer.

• Experience "vibe-coding" or rapidly prototyping with tools like AI Studio, Cursor, Lovable, Replit, or similar AI-assisted development environments.

• Experience with front-end development technologies (HTML, CSS, JavaScript, React, Swift, Jetpack Compose) and coding tools (CLIs, IDEs).

The US base salary range for this full-time position is between $178,000 USD - $265,000 + bonus + equity + benefits.

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You'll own product surfaces end-to-end , from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast.

This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn't typing , it's judgment, taste, and the ability to react to what researchers need next. You'll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you'll do it in a loop that closes in hours and days, not quarters or months.

Anthropic's Reinforcement Learning organization leads the research and development that trains Claude to be capable, reliable, and safe. We've contributed to every Claude model, with significant impact on the autonomy and coding capabilities of our most advanced models. Our work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models.

The RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model. Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible , from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations.

You'll build and extend web platforms for RL environment creation, management, and quality review , including environment configuration, versioning, and validation workflows. You'll develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction. You'll design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early.

You'll build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking. You'll create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure. You'll build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels.

You'll develop onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks. You'll partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products.

You may be a good fit if you have strong software engineering fundamentals and real full-stack range , you're comfortable owning a surface from database schema to frontend. You're proficient in Python and a modern web stack (React, TypeScript, or similar). You have a track record of shipping systems that solved a hard problem, not just shipped on time , e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible.

You operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket. You have found yourself wondering 'why isn't this moving faster?' in previous roles , and then have done something about it. You care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers.

You communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work. You thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before. You care about Anthropic's mission to build safe, beneficial AI and want your work to contribute directly to it.

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You will ground projections in microeconomic signals from the Anthropic Economic Index , usage patterns across millions of real-world AI interactions, surfaced through privacy-preserving measurement , so that scenario forecasts are disciplined by what we actually observe about task transformation and productivity.

Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation.

You will:

• Build macroeconomic models of transformative AI spanning growth, labor markets, and income distribution
• Develop and maintain scenario-based forecasting tools; publish forecasts for GDP, productivity, and unemployment under a range of AI-capability trajectories
• Ground macroeconomic projections in microeconomic data from the Anthropic Economic Index, constraining theory with observed patterns of adoption and task transformation
• Analyze questions of income distribution and economic governance under transformative-AI scenarios
• Contribute to the development of AI-powered research tools for economics
• Contribute to Economic Index Reports and publish Research Briefs on first-order questions as they arise
• Build and maintain relationships with academic institutions, policy think tanks, and other research partners
• Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders

Required skills include proficiency in Python, Julia, or similar for computational economics, facility with AI coding agents as part of a research workflow, and comfort learning new technical tools and frameworks.

The ideal candidate works at the intersection of growth theory, forecasting, and frontier AI, and has a strong interest in ensuring AI development benefits humanity.

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As a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise, you will partner with Scale's Operations team and enterprise customers to translate ambiguity into structured evaluation data. This involves guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.

Your responsibilities will also include analysing feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments. You will design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems, including creating models that critique, grade, and explain agent outputs.

To succeed in this role, you will need a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and the ability to thrive in a dynamic, fast-paced research environment. You should be able to think outside the box, stay current with the latest literature in AI evaluation, and be passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.

In addition to your technical expertise, you will need excellent communication and collaboration skills, as you will work closely with cross-functional teams to drive project success.

If you are a motivated and detail-oriented individual with a passion for AI research and evaluation, we encourage you to apply for this exciting opportunity.

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You will partner with public sector clients to deeply understand their challenges and define AI-driven solutions.

Key responsibilities include:

• Building and deploying end-to-end AI applications into production leveraging latest developments from the biggest AI labs, and open source models
• Collaborating with cross-functional teams, including data annotation specialists, to create high-quality training datasets
• Designing and maintaining robust evaluation frameworks to ensure the reliability and effectiveness of AI models
• Participating in customer engagements, including occasional travel (approximately two weeks per quarter)

Ideally you'd have:

• A strong engineering background, with a Bachelor’s degree in Computer Science, Mathematics, or a related quantitative field (or equivalent practical experience)
• 7+ years of post-graduation engineering experience, with demonstrated proficiency in languages such as Python, TypeScript/JavaScript, Java, or C++
• 2+ years of experience applying AI/ML in production environments, such as deploying deep learning solutions, building generative/agentic AI applications or setting up evaluations pipelines
• Familiarity with cloud-based machine learning tools and platforms (e.g. AWS, GCP, Azure)
• Strong problem-solving skills, with a data-driven approach to iterating on machine learning models and datasets
• Excellent written and verbal communication skills to collaborate effectively in a cross-functional environment

Nice to haves:

• Experience working at a startup, particularly as founding engineer
• Experience building and deploying large-scale AI solutions
• Strong written and verbal communication skills to operate in a cross-functional team environment
• Proficiency in Arabic (if focused on language models)

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We are seeking a highly skilled Staff Software Engineer to join our Public Sector team. As a Staff Software Engineer, you will be responsible for designing and implementing software solutions for the public sector. You will work closely with cross-functional teams to develop and deploy software applications that meet the needs of government agencies.

Responsibilities:

• Design and implement software solutions for the public sector
• Work closely with cross-functional teams to develop and deploy software applications
• Collaborate with stakeholders to understand their needs and develop software solutions that meet those needs
• Develop and maintain software documentation
• Participate in code reviews and ensure that code meets quality standards

Requirements:

• Bachelor's degree in Computer Science or related field
• 5+ years of experience in software development
• Proficiency in programming languages such as Java, Python, or C++
• Experience with Agile development methodologies
• Strong understanding of software design patterns and principles
• Excellent communication and collaboration skills

Preferred Qualifications:

• Master's degree in Computer Science or related field
• 10+ years of experience in software development
• Experience with cloud-based technologies such as AWS or Azure
• Experience with DevOps practices

Benefits:

• Competitive salary and benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment

Salary Range: $252,000-$362,000 USD

Required Skills:

• Full Stack Development
• Cloud-Native Technologies
• Data Engineering
• AI Application Integration
• Problem Solving
• Collaboration and Communication
• Adaptability and Learning Agility

Preferred Skills:

• Experience with modern web development frameworks
• Familiarity with cloud platforms
• Understanding of containerization and container orchestration
• Knowledge of ETL processes
• Understanding of data modeling, data warehousing, and data governance principles
• Familiarity with integrating Large Language Models

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As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, defining the research roadmap and driving execution from early prototyping to deployment. You'll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

Key responsibilities include:

• Leading, mentoring, and growing a team of research scientists and engineers working on GenAI research initiatives
• Defining and driving a multi-year research roadmap, identifying key scientific questions, setting milestones, allocating resources, and ensuring rigorous execution
• Collaborating cross-functionally with engineering, product, client-facing teams, and external academic or industry partners to translate research into components, insights, and actionable outcomes
• Communicating compellingly, publishing research, presenting at conferences, engaging in open-source contributions, and representing the team externally
• Driving an inclusive, high-performing culture, helping your team through technical challenges, providing growth opportunities, and attracting top talent

Ideal candidates will have:

• 5+ years of hands-on research experience in machine learning, deep learning, generative models, agent/RL systems, or related domains
• A strong track record of research excellence, including publications in top-tier ML/AI venues
• Experience leading or managing research teams, mentoring, coaching, and developing talent
• Excellent written and verbal communication skills, articulating research ideas and outcomes to technical and non-technical stakeholders

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As a Solutions Engineer, you will be part of helping shape our early-stage federal business by re-envisioning our commercial product offerings for our federal clients.

Key responsibilities include:

• Becoming an expert on the end-to-end of Scale Products
• Creating tailored demonstrations and collateral for federal stakeholders at both the executive and analyst level
• Partnering with Scale Account Executives to deliver customer pilots according to requirements agreed by the customer
• Integrating and ingesting a variety of external datasets to solve government use cases
• Interacting with customers on a day-to-day basis to understand their pain points and design solutions
• Working with internal product and engineering teams to turn customer requirements into Scale capabilities
• Understanding public sector mission sets and strategic objectives to better showcase Scale's products

Ideal candidates will have:

• A strong engineering background, preferably in computer science, mathematics, or other quantitative fields
• Strong communication skills, ability to interact with both technical and non-technical customers at all levels
• At ease with technology, able to quickly pick up new tech stacks and troubleshoot
• Previous experience working with Public Sector customers
• Proficiency in scripting languages such as Python, Javascript/Typescript, Bash scripts, or programming languages
• A strong desire to roll up your sleeves and help build a business in an extremely fast-paced environment
• Active US Government Security Clearance (TS / SCI required)
• Based in the Washington, DC area or willing to relocate
• Background working in AI/ML, particularly Generative AI and Large Language Models

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

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This high-impact role will drive the technical direction and oversight for Deep Research Agent Development, translating cutting-edge research in Generative AI, Large Language Models (LLMs), and Agentic Frameworks into robust, scalable, and high-impact production systems that enhance enterprise operations, analytics, and core efficiency.

The ideal candidate thrives in a fast-paced environment, has a passion for both deep technical work and mentoring, and is capable of setting a long-term technical strategy for a critical domain while maintaining a strong, hands-on delivery focus.

Responsibilities

Technical Leadership & Vision

• Set the Technical Roadmap: Define and own the technical strategy, architecture, and roadmap for Deep Research Agents for the Enterprise, ensuring alignment with Scale AI’s overall AI strategy and business goals.

• Drive Breakthrough Research to Production: Lead the end-to-end development, from initial research to production deployment, to landing on customer impact, with a focus on integrating diverse data modalities.

• Core Agent Capabilities Development:

Advanced Knowledge Retrieval: Architect and implement state-of-the-art retrieval systems to ensure the agents provide accurate and comprehensive answers from public and proprietary data sources from enterprises.

Data Analysis: Design and champion the development of data analysis agents that accurately translate complex natural language queries into executable SQL/code against diverse enterprise data schemas.

Multimodal Intelligence: Lead the integration of Multimodal AI capabilities to process and extract structured information from visual documents, tables, and forms, enriching the agent's knowledge base.

Architecture & Design: Design and champion highly scalable, reliable, and low-latency infrastructure and frameworks for building, orchestrating, and evaluating multi-agent systems at enterprise scale.

Technical Excellence: Serve as the technical authority for the team, leading design reviews, defining ML engineering best practices, and ensuring code quality, security, and operational excellence for all agent systems.

Team Leadership & Mentorship

• Lead and Mentor: Technically lead and mentor a team of Machine Learning Engineers and Research Scientists, fostering a culture of innovation, rigorous engineering, rapid iteration, and technical depth.

• Recruiting & Growth: Partner with management to hire, onboard, and grow top-tier talent, helping to shape the long-term structure and capabilities of the team.

• Cross-Functional Influence: Collaborate effectively with Product Managers, Data Scientists, and other engineering/science teams to translate ambiguous, high-level business problems into concrete, executable technical specifications and impactful agent solutions.

Basic Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience.

• 8+ years of experience in software development, with at least 6 years focused on Machine Learning, Deep Learning, or Applied Research in a production environment.

• 2+ years of experience in a formal or informal Technical Leadership role (Team Lead, Tech Lead) with a focus on setting technical direction for a domain.

• Deep expertise in Generative AI and Large Language Models (LLMs).

• Demonstrated experience designing, building, and deploying AI Agents or complex Agentic systems in production at scale.

• Experience with large-scale distributed systems and real-time data processing.

Preferred Qualifications

• Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field.

• Demonstrated experience designing and deploying production-grade Text-to-SQL systems, including handling complex schema linking and query optimization.

• Practical experience with Multimodal AI, specifically integrating OCR and vision-language models for document intelligence and structured data extraction from images/forms.

• Proven experience in one or more relevant deep research areas: Reinforcement Learning (RL), Reasoning and Planning, Agentic Systems.

• Experience with vector databases and advanced retrieval techniques.

• A track record of publishing research papers in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD).

• Excellent written and verbal communication skills, with the ability to articulate complex technical vision to executive stakeholders and technical peers.

• Experience driving cross-team technical initiatives that have delivered significant business impact.

Compensation

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

About Us

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

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You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.

Key responsibilities include:

• Building, profiling and optimising our training and inference framework.
• Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Researching and integrating state-of-the-art technologies to optimise our ML system.

The ideal candidate will have:

• Passionate about system optimisation.
• Experience with multi-node LLM training and inference.
• Experience with developing large-scale distributed ML systems.
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.

Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

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About the Role:

We're hiring the first leader of our Partner Success team, which will pick the best consulting and systems integration firms to partner with and make them great. You will build Partner Success at Anthropic from scratch, hiring your first partner success managers, defining how they run their portfolios, and personally carrying a portfolio of partners yourself as your reference implementation.

Responsibilities:

• Build and lead the Partner Success team from scratch.
• Decide which partners get the team's attention.
• Design the team's engagement model.
• Run the joint planning and business review cadence with each managed partner.
• Drive scalable enablement across the managed partner book.
• Drive adoption, retention, and expansion in the joint customer book.
• Drive industry specialization across the managed partner book.
• Steward co-investment funding decisions.
• Interlock with Anthropic's direct sales field and with the alliances organization that owns the executive relationship with each strategic partner.

Requirements:

• Six to ten years of experience working with consulting and systems integration partners at a software company, cloud platform, or partner-led business, with at least two of those years managing a team.
• Built or scaled a partner-facing team from scratch before.
• Deep understanding of how partner success works in a usage-based business.
• Strong commercial instincts on partner selection and co-investment funding.
• Experience running scalable partner or practitioner enablement.
• Enough technical fluency to be credible in an architecture review.

Annual compensation range for this role is $300,000-$355,000 USD.

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At Scale, our mission is to accelerate the development of AI applications. We're looking for a Machine Learning Research Scientist/Engineer to join our team and help us shape the future of AI.

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). You will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning.

Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Responsibilities

• Study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents
• Shape Scale's data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning
• Contribute to impactful research on language model reasoning
• Collaborate with external researchers
• Work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions

Requirements

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow
• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.)
• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning
• Strong written and verbal communication skills, along with the ability to work effectively across teams

Nice to Have

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX
• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents
• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar
• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH
• Proficiency in cloud-based ML development, with experience in AWS or GCP environments

Benefits

• Comprehensive health, dental and vision coverage
• Retirement benefits
• A learning and development stipend
• Generous PTO
• Commuter stipend

Salary Range

$252,000-$315,000 USD

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Scale's ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.

At Scale, we're uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale's ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.

Responsibilities:

• Build, profile and optimize our training and inference framework
• Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Ideal Candidate:

• Strong excitement about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
• Strong written and verbal communication skills and the ability to operate in a cross functional team environment

Nice to Have:

• Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation Packages:

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

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You'll connect deeply with customer stakeholders to understand their business goals, technical environments, and operational challenges, and architect intelligent solutions that combine the power of LLMs, SLMs, and real-time AI assistance. Your ability to translate both technical complexity and business impact will be critical to driving successful sales cycles and long-term customer outcomes.

Responsibilities:

• Act as a consultative partner to customers,uncovering business objectives, technical environments, and operational challenges to map them to AI-driven solutions.
• Act as a consultative advisor to prospective customers, uncovering operational workflows and strategic goals to design solutions leveraging Cresta’s real-time AI capabilities, including virtual agents, agent assist, and conversation intelligence.
• Lead technical discovery sessions to understand customer systems, including contact center infrastructure, telephony and IVR architecture, and CRM/workforce management platforms.
• Qualify and translate customer requirements into robust, scalable Cresta configurations, ensuring tight alignment with business value and technical feasibility.
• Design and deliver compelling, tailored product demonstrations that highlight how Cresta’s AI-powered virtual agents, real-time coaching, and analytics can deliver measurable business outcomes.
• Own the technical design and delivery of proof-of-value (POV) engagements, including integrations, real-time coaching workflows, and virtual agent use cases.
• Run ROI workshops and build business case models that connect Cresta’s capabilities to quantifiable customer impact (e.g., cost reduction, efficiency, CSAT).
• Provide insights based on your experience with AI technologies, contact center transformation, and customer success strategies.
• Serve as a technical liaison between Sales, Product, and Engineering,providing feedback on platform capabilities, customer needs, and market trends in AI, NLP, and contact center transformation.
• Stay current on emerging technologies, including LLMs, SLMs, retrieval-augmented generation (RAG), speech recognition, and contact center AI platforms.
• Deliver persuasive, tailored product demonstrations that showcase how Cresta’s AI,built on a proprietary architecture using large and small language models,drives measurable ROI through automation, efficiency, and improved customer outcomes.

Qualifications:

• 7+ years of experience in customer-facing roles, including 1–3 years in pre-sales, solutions engineering, or consulting within the enterprise software or contact center industry.
• Deep knowledge of contact center solutions, including telephony architecture (SIP, SBCs, ACDs, IVRs) and CCaaS platforms (e.g., Genesys, NICE, Five9, Amazon Connect).
• Strong understanding of AI/ML technologies, especially large language models (LLMs), small language models (SLMs), and how they are applied in conversational AI and agent augmentation.
• Experience with real-time systems, CRM tools (e.g., Salesforce), analytics platforms, and SaaS solution architecture.
• Ability to design and communicate complex solutions clearly to both technical and business audiences.
• Consultative mindset with a proven track record of leading strategic conversations, influencing stakeholders, and tailoring solutions to business goals.
• Fast learner and self-starter who thrives in high-growth, high-collaboration environments.
• Enthusiastic about Cresta’s mission and motivated to help customers unlock value from AI.

Perks & Benefits:

We offer a comprehensive and people-first benefits package to support you at work and in life:

• Comprehensive medical, dental, and vision coverage with plans to fit you and your family
• Flexible PTO to take the time you need, when you need it
• Paid parental leave for all new parents welcoming a new child
• Retirement savings plan to help you plan for the future
• Remote work setup budget to help you create a productive home office
• Monthly wellness and communication stipend to keep you connected and balanced
• In-office meal program and commuter benefits provided for onsite employees

Compensation at Cresta:

Cresta’s approach to compensation is simple: recognize impact, reward excellence, and invest in our people. We offer competitive, location-based pay that reflects the market and what each individual brings to the table. The posted base salary range represents what we expect to pay for this role in a given location. Final offers are shaped by factors like experience, skills, education, and geography. In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family. This role is variable target compensation eligible. There is potential to exceed target earnings when goals are surpassed.

Base Salary Range: $180,000–$205,000 + variable & Offers Equity

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You will:

Lead the design and implementation of scalable backend systems and distributed architectures for Federal customers. Manage the full lifecycle of feature development from requirement definition to deployment on classified networks. Direct the orchestration of asynchronous agent fleets to meet mission requirements. Lead customer engagements to translate mission needs into technical requirements. Own the communication with stakeholders to ensure implementation meets defined acceptance criteria. Conduct technical reviews and identify risks within machine learning infrastructure and model serving. Drive the platform roadmap by providing technical specifications for Federal product offerings.

Ideally you will have:

Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial. Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

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Your ability to translate both technical complexity and business impact will be critical to driving successful sales cycles and long-term customer outcomes.

Responsibilities: Act as a consultative partner to customers,uncovering business objectives, technical environments, and operational challenges to map them to AI-driven solutions. Act as a consultative advisor to prospective customers, uncovering operational workflows and strategic goals to design solutions leveraging Cresta's real-time AI capabilities, including virtual agents, agent assist, and conversation intelligence. Lead technical discovery sessions to understand customer systems, including contact center infrastructure, telephony and IVR architecture, and CRM/workforce management platforms. Qualify and translate customer requirements into robust, scalable Cresta configurations, ensuring tight alignment with business value and technical feasibility. Design and deliver compelling, tailored product demonstrations that highlight how Cresta's AI-powered virtual agents, real-time coaching, and analytics can deliver measurable business outcomes. Own the technical design and delivery of proof-of-value (POV) engagements, including integrations, real-time coaching workflows, and virtual agent use cases. Run ROI workshops and build business case models that connect Cresta's capabilities to quantifiable customer impact (e.g., cost reduction, efficiency, CSAT). Provide insights based on your experience with AI technologies, contact center transformation, and customer success strategies. Serve as a technical liaison between Sales, Product, and Engineering,providing feedback on platform capabilities, customer needs, and market trends in AI, NLP, and contact center transformation. Stay current on emerging technologies, including LLMs, SLMs, retrieval-augmented generation (RAG), speech recognition, and contact center AI platforms. Deliver persuasive, tailored product demonstrations that showcase how Cresta's AI,built on a proprietary architecture using large and small language models,drives measurable ROI through automation, efficiency, and improved customer outcomes.

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This role sits at the intersection of research and large-scale systems engineering: a builder who understands both the algorithms behind RL and the realities of distributed training and inference at scale. You'll wear many hats, from optimising rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers and infra teams to make reinforcement learning stable, fast, and production-ready.

Responsibilities:

• Design, build, and optimise the infrastructure that powers large-scale reinforcement learning and post-training workloads.

• Improve the reliability and scalability of RL training pipeline, distributed RL workloads, and training throughput.

• Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.

• Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.

• Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.

• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

We're looking for someone with strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases. You should have a good understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.

Experience training or supporting large-scale language models with tens of billions of parameters or more is a plus. Familiarity with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry) is also a plus.

Logistics:

• Location: This role is based in San Francisco, California.

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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Location: London, UK

Department: Sales

As the Manager of the International Partnerships, Applied AI, Solutions Architect team at Anthropic, you will drive the adoption of frontier AI by enabling the deployment of Anthropic's products (Claude for Enterprise, Claude Code, and API) through our Global and Regional System Integrators (GSIs/RSIs), cloud partners (AWS and GCP), and strategic technology partners across international markets.

Based in London, you will lead and grow a team of Partner Solutions Architects, establishing Anthropic's technical partner ecosystem across EMEA and beyond. You'll be responsible for leading & growing the International Partnerships Applied AI team, establishing processes and best practices for partner-led pre-sales engagements, helping each team member achieve success, high productivity, and career growth, and representing Anthropic as a technical lead on some of its most important international partnerships.

In collaboration with the Sales, Partnerships, Product, and Engineering teams, you'll help partners incorporate leading-edge AI systems into their practices, solutions, and customer engagements. You will employ your excellent communication skills to explain and demonstrate complex solutions persuasively to technical and non-technical audiences alike.

You will play a critical role in identifying opportunities to accelerate indirect revenue, enable partner AI practices, and execute on long-term international GTM strategy, while maintaining our best-in-class safety standards.

Responsibilities:

• Team Leadership & Development: Manage and mentor a team of Applied AI, Partner Solutions Architects, providing both technical guidance and career development. Set goals and reviews for your team, promoting growth and output

• Strategic Technical Partnership: Serve as the senior technical thought partner to the Anthropic international GTM partnerships team, providing technical expertise to better understand the partner landscape, driving key strategic programs, and identifying opportunities to deepen partner technical capabilities across international markets

• Partner Ecosystem Enablement: Embed your team with GSI and cloud partner technical teams to enable their AI practices, support troubleshooting, evangelize Anthropic in their developer communities, and serve as an escalation point for complex technical issues

• Joint Solution Development: Lead your team in collaborating with partners to identify high-value industry-specific GenAI applications, develop joint solutions, and codify reference architectures / best practices to accelerate time to deployment across international markets

• Customer Deal Support: Own the technical portions of partner-led pre-sales engagements, ensuring your team intervenes directly to unblock strategic customer deals where partners are the primary delivery vehicle, providing deep technical expertise and solution architecture guidance

• Partner Ecosystem & Events: Represent Anthropic at international partner events such as GSI customer workshops, AWS summits, and industry conferences. Lead or support partner-specific developer events, hackathons, and technical enablement sessions

• Cross-Functional Collaboration: Drive collaboration from cross-functional teams to influence and unify stakeholders at all levels of the organization to drive business outcomes. Partner closely with your aligned GTM leadership to co-build international partner strategies

• Product Feedback: Validate and gather feedback on Anthropic's products and offerings, especially as they relate to international partner use cases and deployment patterns, and deliver this feedback to relevant Anthropic teams to inform product roadmap and partner strategy

• Thought Leadership: Contribute to thought leadership through conference presentations, webinars, and technical content creation focused on the international partner ecosystem

You may be a good fit if you:

• 7+ years of experience in technical customer-facing/partner-facing roles such as Solutions Architect, Sales Engineer, Partner Sales Engineer, Technical Account Manager

• 5+ years of technical go-to-market management experience, specifically managing pre-sales or partner-facing technical teams across EMEA, APAC, and other international regions.

• Track record of successfully building and scaling partnerships with GSIs (e.g., Accenture, Deloitte, WPP, TCS, Infosys) and/or cloud providers (AWS, GCP) to solve complex technical challenges across international markets

• Experience with the unique dynamics of partner-led selling and delivery, including indirect revenue models and partner enablement at scale

• Deep technical proficiency with enterprise AI deployments, API integrations, and production LLM use cases

• Exceptional ability to build relationships with and communicate technical concepts to diverse stakeholders including C-suite executives, engineering & IT teams, and partner leadership

• Have an organizational mindset and enjoy building foundational teams in a relatively unstructured environment

• Have excellent communication, collaboration, and coaching abilities

• Are comfortable dealing with highly uncertain, ambiguous, and fast-moving environments

• Strong executive presence and ability to foster deep relationships with technical leaders and partner engineering teams

• Have at least a high-level familiarity with the architecture and operation of large language models and/or ML in general

• Experience with prompt engineering, LLM evaluation, and architecting AI-powered systems

• A love of teaching, mentoring, and helping others succeed

• Have a passion for making powerful technology safe and societally beneficial

• Think creatively about the risks and benefits of new technologies, and think beyond past checklists and playbooks

Strong candidates may have:

• Partner SA Leadership at Scale: 5+ years leading partner-facing solution architect teams through hypergrowth, with direct experience managing both senior SAs and developing junior talent in complex partner ecosystem environments

• AI/ML Technical Depth + Executive Engagement: Hands-on experience with AI/ML platforms and enterprise integration patterns, combined with proven track record engaging C-level stakeholders and partner leadership in large-scale technical evaluations and joint GTM motions

• GSI Practice Building: Experience helping GSIs or consultancies build or scale their AI/ML practices, including enablement programs, certification paths, and joint solution development

Annual compensation range for this role is £170,000-£215,000 GBP.

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This is a unique opportunity to shape how frontier AI models handle dual-use cybersecurity knowledge,balancing the tremendous potential of AI to advance legitimate security research and defensive capabilities while preventing misuse by malicious actors.

In this role, you will lead and grow a team of technical specialists focused on cyber threat modeling and evaluation frameworks. You will serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies.

You will collaborate closely with internal and external threat modeling experts to develop training data for safety systems, and with ML engineers to train these systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers.

You will also analyze safety system performance in traffic, identifying gaps and proposing improvements. You will conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks.

You will develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces. You will partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle.

You will translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies. You will contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety.

You will monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these.

You will mentor and develop team members, fostering a culture of technical excellence and responsible AI development.

To be successful in this role, you will need to have:

• An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity
• 5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing
• 2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders
• Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
• Deep expertise in modern cybersecurity, including both offensive techniques (vulnerability research, exploit development, penetration testing, malware analysis) and defensive measures (detection, monitoring, incident response)
• Demonstrated ability to create threat models and translate technical cyber risks into policy frameworks
• Familiarity with responsible disclosure practices, vulnerability coordination, and cybersecurity frameworks (e.g., MITRE ATT&CK, NIST Cybersecurity Framework, CWE/CVE systems)
• Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders
• Experience developing policies or guidelines at scale, balancing safety concerns with enabling legitimate use cases
• A passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies
• Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve
• Track record of translating specialized technical knowledge into actionable safety policies or enforcement guidelines

Preferred qualifications include:

• Background in AI/ML systems, particularly experience with large language models
• Experience developing ML-based security systems or adversarial ML research
• Experience working with defense, intelligence, or security organizations (e.g., NSA, CISA, national labs, security contractors)
• Published security research, disclosed vulnerabilities, or participated in bug bounty programs
• Understanding of Trust & Safety operations and content moderation at scale
• Certifications such as OSCP, OSCE, GXPN, or equivalent demonstrating technical depth
• Understanding of dual-use security research concerns and ethical considerations in AI safety

The annual compensation range for this role is $320,000-$405,000 USD.

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At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process.

Your Impact

• Advance the field of AI alignment by developing cutting-edge methods, such as RLHF and novel approaches, that ensure AI systems reflect human preferences more accurately.

• Improve the quality of human-in-the-loop data by designing and deploying rigorous measurement and enhancement systems, leading to more reliable AI training.

• Increase efficiency and effectiveness in AI-assisted data labeling by creating tools that leverage active learning and adaptive sampling, reducing manual effort while improving accuracy.

• Shape the next generation of AI models by investigating how different types of human feedback (e.g., demonstrations, preferences, critiques) impact model performance and alignment.

• Optimize human feedback collection by developing novel algorithms that enhance how AI learns from human input, improving model adaptability and responsiveness.

• Bridge research and real-world application by integrating breakthroughs into Labelbox’s product suite, making human-AI alignment techniques scalable and impactful for users.

• Drive industry innovation by engaging with customers and the broader AI community to understand evolving data needs and share best practices for training frontier models.

• Contribute to the AI research ecosystem by publishing in top-tier journals, presenting at leading conferences, and influencing the future of human-centric AI.

• Stay ahead of AI advancements by continuously exploring new frontiers in human-AI collaboration, human data quality, and AI alignment, keeping Labelbox at the cutting edge.

• Establish Labelbox as a thought leader in AI by creating technical documentation, blog posts, and educational content that shape the industry's approach to human-centric AI development.

What You Bring

• A strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field.

• Proven experience (3+ years) in solving complex ML challenges and delivering impactful solutions that improve real-world AI applications.

• Expertise in designing and implementing data quality measurement and refinement systems that directly enhance model performance and reliability.

• A deep understanding of frontier AI models,such as large language models and multimodal models,and the human data strategies needed to optimize them.

• Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow to prototype and develop cutting-edge solutions.

• A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community.

• The ability to bridge research and application by interpreting new findings and rapidly translating them into functional prototypes.

• Strong analytical and problem-solving skills that enable you to tackle ambiguous AI challenges with structured, data-driven approaches.

• Exceptional communication and collaboration skills, allowing you to work effectively across multidisciplinary teams and with external stakeholders.

Labelbox Applied Research

At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.

We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.

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You are a Systems Engineer at heart, but you are ready to apply LLMs, RAG, and predictive modeling to solve infrastructure challenges at scale.

Our Team's Stack:

• Languages: Python, Go, bash/sh
• Observability: Prometheus, Victoria Metrics, Grafana
• OS & Kernel: Linux Kernel (custom build), Ubuntu
• Hardware: Intel/AMD/ARM CPUs, Nvidia GPUs, DPUs, Infiniband and Ethernet NICs
• Containerization: Docker, Kubernetes (k8s), KubeVirt, containerd, kubelet

Responsibilities:

• Pipeline Architecture: Design, maintain, and automate reproducible OS image build pipelines for our massive fleet of GPU-accelerated servers.
• Kernel Distribution: Collaborate with kernel engineers to package, validate, and distribute custom Linux builds across Intel, AMD, and ARM architectures.
• Dependency Management: Build tooling to manage dependencies, versioning, and release workflows, ensuring hermetic builds.
• Telemetry & Metrics: Standardize the collection of build metrics to create a baseline for future AI modeling.
• 'Smart' CI/CD & Auto-Remediation: Architect AI agents that ingest and analyze build logs in real-time. Develop systems that auto-triage errors, categorize failure patterns, and generate context-aware fix suggestions for engineering teams.
• Predictive Regression Modeling: Design ML workflows that utilize historical performance data to detect kernel and OS regressions (latency, throughput, stability) in staging environments before they impact production.
• Dynamic Kernel Tuning: Implement closed-loop feedback systems that analyze real-time system metrics and automatically suggest or apply sysctl parameter optimizations for specific customer workloads.
• Next-Gen ChatOps: Engineer LLM-driven interfaces for Slack/internal tools, enabling stakeholders to query build statuses, request log summaries, or provision resources using natural language commands.

Requirements:

• 4+ years of professional experience in Linux Systems Engineering, Release Engineering, or DevOps.
• Deep knowledge of Linux internals (boot process, kernel modules, networking stack).
• Experience with package management (Debian/Ubuntu) and build systems.
• Strong proficiency in Python (essential for the AI integration aspects of this role).
• Demonstrable experience integrating API-based AI models (OpenAI, Anthropic, or local open-source models) into software workflows.
• Understanding of RAG (Retrieval-Augmented Generation) architectures for querying technical documentation or logs.
• Experience building event-driven automation (e.g., using webhooks to trigger analysis agents).
• Familiarity with data structures required for vector search or time-series analysis.

Nice-to-haves:

• Experience with Kubeflow or MLFlow.
• Background in High-Performance Computing (HPC).
• Experience fine-tuning small language models (SLMs) for code or log analysis tasks.

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Responsibilities:

• Design, develop, and deploy Cresta's AI Agent solutions and proprietary models.
• Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios.
• Collaborate with cross-functional teams including front-end and back-end software engineers to integrate AI Agents into Cresta's customer solutions.
• Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases.
• Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta's AI Agents.
• Innovate and research ways to improve security, cost-efficiency, and reliability of AI systems.

Qualifications:

• Bachelor's or Master's Degree in Computer Science, Mathematics, or a related field.
• 2+ years of hands-on industry experience with AI and machine learning, preferably with experience working with LLMs in large-scale production environments.
• Solid knowledge of machine learning concepts and methods, especially those related to NLP, Generative AI, and working with LLMs.
• Practical knowledge of modern machine learning frameworks and technologies (e.g., PyTorch, Tensorflow, Hugging Face, NumPy), as well as experience with distributed systems and cloud-based AI infrastructure.
• Strong problem-solving and strategic thinking abilities, with a proven ability to lead cross-functional teams and work collaboratively to deliver innovative AI solutions in production.
• A passion for driving AI adoption and pushing the boundaries of AI technology into real-world applications, with an ability to mentor junior engineers and influence strategic decisions across the organization.

Perks & Benefits:

• We offer Cresta employees a variety of medical, dental, and vision plans, designed to fit you and your family's needs.
• Paid parental leave to support you and your family.
• Monthly Health & Wellness allowance.
• Work from home office stipend to help you succeed in a remote environment.
• Lunch reimbursement for in-office employees.
• PTO: 3 weeks in Canada.

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We're hiring research engineers to define and execute the Labs research agenda. You'll scope your own projects, run experiments end-to-end, and decide when an idea is ready to hand off to a production team , or when to kill it and move on.

Responsibilities:

• Lead and contribute to research projects investigating new methods for detecting misuse of Claude, identifying malicious organisations and accounts, strengthening model safeguards, and other safety needs.

• Design and run offline analyses over model usage data to surface abuse patterns, build classifiers and detection systems, and evaluate their effectiveness.

• Develop and iterate on prototypes that could eventually feed signals into the real-time safeguards path, partnering with engineers on tech transfer.

• Contribute to a broader research portfolio investigating methods for detecting abusive behaviour in chat-based or agentive workflows, and for training the model to robustly refrain from dangerous responses or behaviours without over-refusing.

• Build evaluations and methodologies for measuring whether safeguards actually work, including in agentic settings.

• Write up findings clearly so they inform decisions across Trust & Safety, research, and product teams.

You may be a good fit if you:

• Have a track record of independently driving research projects from ambiguous problem statements to concrete results, ideally in AI, ML, security, integrity, or a related technical field.

• Are comfortable scoping your own work and switching between research, engineering, and analysis as a project demands.

• Have working familiarity with how large language models operate , sampling, prompting, training , even if LLMs aren't your primary background.

• Are proficient in Python and comfortable working with large datasets.

• Care about the societal impacts of AI and want your work to directly reduce real-world harm.

Strong candidates may also have:

• Experience building and training machine learning models, including classifiers for abuse, fraud, integrity, or security applications.

• Knowledge of evaluation methodologies for language models and experience designing evals.

• Experience with agentic environments and evaluating model behaviour in them.

• Background in trust and safety, integrity, fraud detection, threat intelligence, or adversarial ML.

• Experience with red teaming, jailbreak research, or interpretability methods like steering vectors.

• A history of taking research prototypes and transferring them into production systems.

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Benefits

• Competitive compensation and benefits

• Optional equity donation matching

• Generous vacation and parental leave

• Flexible working hours

• Lovely office space in which to collaborate with colleagues

Visa Sponsorship

• We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You'll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You'll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.

Responsibilities

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.
• Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.

Preferred qualifications:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Track record of improving research productivity through infrastructure design or process improvements.
• Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
• Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
• Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).
• Contributions to open-source GPU, ML systems, or compiler optimization projects.
• Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.

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Responsibilities:

• Lead and execute complex data science projects that directly advance our drug development portfolio
• Develop and implement sophisticated models for therapeutic hypothesis evaluation, including patient stratification and biomarker identification
• Design and create AI models for modernizing clinical trial evaluations, including surrogate endpoints
• Aid in the development and training of AI agents to automate and optimize biomedical workflows
• Collaborate cross-functionally with clinical, technical, and research teams
• Present complex analytical findings to senior stakeholders, including executive leadership

About You:

• Required Qualifications:

+ PhD in computational sciences or life sciences + 3+ years of post-academic experience in life sciences (biotech, pharma, consulting) + Strong programming skills, particularly in Python + Extensive experience in multi-modal bioinformatics analysis

• Preferred Qualifications:

+ Proven expertise in cloud computing environments, including proficiency with tabular and/or graph databases + Strong background in machine learning and deep learning, particularly in biological applications + Experience with large language models (LLM) + Demonstrated ability to collaborate effectively with engineering teams on production systems + Strong communication skills with proven ability to present complex technical findings to senior stakeholders

Total Compensation Range: $170,000 - $215,000

Where We Hire:

Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered.

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You will work at the intersection of AI safety and CBRN security, conducting thorough investigations into potential misuse cases, developing novel detection techniques, and building robust defenses against threat actors who may attempt to leverage our AI technology for developing weapons, synthesizing dangerous compounds, or creating biological harm.

Important context: In this position you may be exposed to explicit content spanning a range of topics, including those of a sexual, violent, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays.

Responsibilities:

• Detect and investigate attempts to misuse Anthropic's AI systems for developing, enhancing, or disseminating CBRN-E weapons, pathogens, toxins, or other threats to harm people, critical infrastructure, or the environment

• Conduct technical investigations using SQL, Python, and other tools to analyze large datasets, trace user behavior patterns, and uncover sophisticated CBRN-E threat actors

• Develop CBRN-E-specific detection capabilities, including abuse signals, tracking strategies, and detection methodologies tailored to dual-use research concerns

• Create actionable intelligence reports on CBRN-E attack vectors, vulnerabilities, and threat actor TTPs leveraging AI systems

• Conduct cross-platform threat analysis grounded in real threat actor behavior, open-source research, and publicly reported programs

• Collaborate with policy and enforcement teams to make informed decisions about user violations and ensure appropriate mitigation actions

• Engage with external stakeholders including government agencies, regulatory bodies, scientific organizations, and biosecurity/chemical security research communities

• Inform safety-by-design strategies by forecasting how threat actors may leverage advances in AI technology for CBRN-E purposes

You may be a good fit if you

• Have deep domain expertise in biosecurity, chemical defense, biological weapons non-proliferation, dual-use research of concern (DURC), synthetic biology, or related CBRN-E threat domains

• Have demonstrated proficiency in SQL and Python for data analysis and threat detection

• Have experience with threat actor profiling and utilizing threat intelligence frameworks

• Have hands-on experience with large language models and understanding of how AI technology could be misused for CBRN-E threats

• Have excellent stakeholder management skills and ability to work with diverse teams including researchers, policy experts, legal teams, and external partners

• Can present analytical work to both technical and non-technical audiences, including government stakeholders and senior leadership

Strong candidates may also have

• Advanced degree (MS or PhD) in biological sciences, chemistry, biodefense, biosecurity, or related field

• Real-world experience countering weapons of mass destruction or other high-risk asymmetric threats

• Experience working with government agencies or in regulated environments dealing with sensitive CBRN-E information

• Background in AI safety, machine learning security, or technology abuse investigation

• Familiarity with synthetic biology, biotechnology, or dual-use research

• Experience building and scaling threat detection systems or abuse monitoring programs

• Active Top Secret security clearance

The annual compensation range for this role is $230,000-$290,000 USD.

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A typical day for this role will involve envisioning, championing, and supporting the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems. You will lead, mentor, challenge, and grow enthusiastic, collaborative software engineers and applied scientists across the organization. You will also raise the AI/ML skillset within the organization, which requires a passion for teaching and mentoring.

To be successful in this role, you will need 12+ years of software engineering experience, with 2+ years of experience in a Principal or Senior Staff Engineer role having ownership responsibility over large-scale software systems. You should have a background in the design and development of scalable AI and ML systems and services, and a deep passion for building ML-powered products.

As a Senior Staff Machine Learning Engineer, you will be an inspiring colleague, a coach, and mentor with experience owning and fostering engineering maturity in multiple organizations. You will be a builder, and implementer, who seeks out high-impact work, and who is proactive, curious, and an excellent communicator.

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We're excited to offer PhD machine learning engineering internships for the summer of 2026. As an intern, you'll contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.

Responsibilities

• Develop and deploy large-scale machine learning systems that drive significant business value across various domains.
• Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
• Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.
• Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.
• Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.

Who We're Looking For

• A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2026 or spring/summer 2027.
• Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn.
• Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.
• Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.
• A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.

What We Offer

• Join us for an unforgettable summer internship and help shape the future of global commerce.
• At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.

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You'll solve hard problems in building reliable, secure, and scalable AI-powered chat workflows so customers can plan, write, review, and secure code faster, with confidence.

This is a hands-on technical leadership role where you'll set direction for how we integrate and evolve large language model providers (including Google Vertex AI) across Ruby on Rails and Python services, raise the bar on observability and testing, and guide the team through ambiguous, high-impact technical decisions.

Over your first year, you'll be expected to drive key architectural choices, reduce technical debt that slows iteration, and help the team ship durable improvements to response quality, reliability, and maintainability.

Some examples of our projects:

• Integrate new generative AI models and providers into GitLab Duo Chat to expand capabilities and improve response quality

• Improve debugging, observability, and test coverage for AI-powered chat workflows to increase reliability at scale

Responsibilities

• Define the technical architecture and technical roadmap for the Duo Chat group, aligning backend execution with product direction and engineering priorities

• Solve the highest-scope and most ambiguous backend problems, delivering secure, well-tested, performant solutions with minimal guidance

• Integrate and extend generative AI capabilities in GitLab Duo Chat, including large language models (LLMs) and providers such as Google Vertex AI

• Develop, ship, and maintain backend features across Python and Ruby on Rails services that power Duo Chat experiences across the GitLab platform

• Design, implement, and review GraphQL application programming interface (API) contracts and supporting backend logic to ensure reliability, scalability, and clear frontend integrations

• Improve observability, debugging workflows, and incident readiness by strengthening logging, tracing, and production troubleshooting practices

• Drive code quality and long-term maintainability by setting internal standards, leading code reviews, and identifying and reducing technical debt

• Mentor engineers across the team and participate in Tier 2 on-call rotations, contributing to root cause analysis and follow-up improvements to resiliency and testing (including RSpec)

Requirements

• Production experience building and operating backend services in Python, including background jobs, APIs, and data models

• Ability to define and evolve technical architecture by weighing trade-offs, selecting patterns and tools, and setting a clear technical direction for others to follow

• Experience setting and driving a technical roadmap in partnership with product and engineering stakeholders

• Proficiency designing and maintaining REST and/or GraphQL APIs with attention to scalability, maintainability, and backward compatibility

• Hands-on experience integrating large language models into applications, including prompt design and building features powered by generative AI

• Strong SQL skills and experience working with relational databases such as PostgreSQL, including efficient queries and data modeling

• Experience mentoring engineers through code review, architectural guidance, and shared standards, and communicating complex technical decisions in a clear, async-first way

• Comfort contributing in a mature codebase across Python and Ruby on Rails, with openness to learning and applying transferable skills from related technologies or domains

About the Team

The Duo Chat team sits within GitLab's AI Engineering organization and is responsible for building and evolving GitLab Duo Chat, the AI-powered chat experience embedded across the GitLab DevSecOps platform.

You'll work with a small, cross-functional group of backend, frontend, and AI specialists who collaborate asynchronously across time zones, using GitLab issues, merge requests, and documentation as the primary way of working.

The team focuses on integrating and scaling generative AI capabilities (including providers like Google Vertex AI), improving reliability and performance, and strengthening debugging, observability, and testing workflows so customers can safely use AI to plan, write, review, and secure their code across GitLab.

How GitLab Supports Full-Time Employees

• Benefits to support your health, finances, and well-being

• Flexible Paid Time Off

• Team Member Resource Groups

• Equity Compensation & Employee Stock Purchase Plan

• Growth and Development Fund

• Parental leave

• Home office support

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You will work on real-world projects, collaborate with experienced professionals, gain valuable experience in the fintech industry, and realise business and social impact. This role requires hybrid work from our Mountain View office, with 2 days a week in person. This internship will pay $40 per hour, with an expected 40 hours per week for the 12-week program.

Responsibilities:

• Train and fine-tune large-scale Foundation Models to support various fintech product use cases
• Work with a large dataset, including structured and unstructured data
• Help in ensuring improvements in our current ML systems via model, data, or experimentation upgrades
• Gain hands-on experience with a wide array of technologies, including PyTorch, AWS, Kafka, Databricks, etc

Requirements:

• Actively pursuing a Master's or PhD in Computer Science, Information Technology, or a related field
• Located in Mountain View, or have the ability to relocate there, for the duration of the internship
• Strong understanding of statistical models, familiarity, and in-depth understanding of machine learning and deep learning algorithms. Familiarity with training or fine-tuning large-scale models, Sequence Transformer models
• Interest in multimodal or multitask learning across structured, sequential, and behavioural data
• Familiarity with AI tools, harness engineering, agentic workflow, etc.
• Hands-on programming experience in Python and ML frameworks such as PyTorch
• Equipped with good verbal and written communication skills
• A background demonstrating strong problem-solving skills
• Committed to taking ownership of projects, conducting thorough investigations, and driving initiatives to conclusion

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The ideal candidate will have deep knowledge of current privacy and technology trends, with a strong passion for data governance/management and AI/ML. They will have demonstrated experience in ensuring privacy-by-design principles are applied throughout the design, construction, and operation of digital products and services at scale.

Responsibilities include leading high-impact initiatives and defining technical requirements for compliance and the responsible use of technology at Airbnb. The candidate will work closely with the Chief Privacy Officer, Legal, and other Privacy & Data Security Team members, as well as engineering and data science teams.

Key responsibilities include:

• Collaborating with technical teams in the identification and effective management of company-wide risks to privacy and the responsible use of technology
• Leading definition and implementation of company-wide standards, practices, and patterns to protect and manage personal data in accordance with privacy and AI regulations
• Working with Legal, Data Science, Data Governance, and InfoSec to introduce Privacy by Design principles in company products and infrastructure
• Creating privacy training for technical roles, including data engineers, developers, and data scientists

Requirements include:

• 15+ years of total experience, with 5+ years of experience in technical program/project management or privacy engineering focused on building technology products and/or systems
• Deep understanding of large-scale, “Big Data” data stores and technologies
• Strong familiarity with the AI/ML development lifecycle: from data collection and curation, through model architecture selection, training, testing, A/B testing, and deployment
• Solid understanding of Large Language Models (LLMs), Generative AI and AI Agents, including compliance and responsible use challenges arising from their deployment in B2C services
• Strong familiarity with Privacy Enhancing Technologies (PETs), such as various types of encryption, de-identification methods (e.g., k-anonymity, differential privacy), and AI/ML interpretability techniques (e.g., SHAP, LIME)

Preferred qualifications include:

• Professional certifications such as Certified Information Privacy Professional (CIPP), Certified Information Privacy Manager (CIPM), or AI Governance Professional (AIGP) or equivalent
• BA/BS and/or advanced degree in engineering, computer science, mathematics, statistics, physics, or a related field
• Experience with programming languages and tools commonly used in AI, such as R, Python and Github

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager.

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The program is open to individuals with a strong technical background in computer science, mathematics, or physics, and who are motivated by making sure AI is safe and beneficial for society as a whole. You will be part of a diverse team of researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

As a fellow, you will have the opportunity to work on projects in select AI safety research areas, such as scalable oversight, adversarial robustness and AI control, model organisms, model internals/mechanistic interpretability, and AI welfare. You will also have access to our Alignment Science and Frontier Red Team blogs, which feature past projects and research directions.

To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program. We are not currently able to sponsor visas for fellows.

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We are seeking a highly skilled and experienced Staff Software Engineer to join our Communication Products team. As a Staff Engineer, you will be responsible for leading the technical vision for ML-powered messaging features, architecting and delivering intelligent capabilities end-to-end, and partnering deeply with ML and product teams.

The Difference You Will Make:

As a Staff Engineer on the team, you will define and drive the technical strategy for integrating ML capabilities into Airbnb's messaging products, including smart replies, message classification, content moderation, translation, and conversational assistance. You will also own the full lifecycle of ML-powered features: from prototyping and experimentation through launch, monitoring, and iteration.

A Typical Day:

• Design, build, and operate the systems that serve ML models within the messaging stack, with a focus on latency, reliability, and scalability
• Write and review technical designs that solve large, open-ended problems at the intersection of ML and product engineering without clearly-known solutions
• Partner with ML, data science, and product teams to identify high-value opportunities, establish evaluation criteria, and close the gap between offline model performance and production impact
• Collaborate with other engineers and cross-functional partners across Messaging, Trust & Safety, Localization, and Platform organizations to align on long-term technical solutions
• Mentor, guide, advocate, and support the career growth of individual contributors
• Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation

Your Expertise:

• 9+ years of relevant engineering hands-on work experience
• Bachelors, Masters, or PhD in CS or related field
• Demonstrated experience building and shipping ML-powered product features in production environments, including model serving, feature pipelines, online/offline evaluation, and monitoring
• Exceptional architecture abilities and experience with architectural patterns of large, high-scale applications
• Familiarity with NLP/NLU techniques and large language models, particularly as applied to messaging, conversational AI, or content understanding
• Shipped several large-scale projects with multiple dependencies across teams, specifically at the intersection of ML infrastructure and product engineering
• Technical leadership and strong communication skills with the ability to translate between ML research, product goals, and engineering execution
• Experience operating distributed, real-time systems at scale with high reliability requirements
• Experience with real-time messaging systems or event-driven architectures
• Familiarity with ML infrastructure at scale (e.g., feature stores, model registries, online inference platforms)
• Prior work on trust & safety, content moderation, or internationalization in a messaging context
• Experience with LLM-based product features, including prompt engineering, retrieval-augmented generation, or fine-tuning

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range: $204,000-$255,000 USD

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Responsibilities:

• Collaborate with AI platform developers, security architects, and internal stakeholders to distill feature asks, gather feedback, and ensure product-market fit for AI agent identity.
• Define and own the product strategy and roadmap for AI agent-specific capabilities, including agent-to-agent authentication and delegated authority.
• Identify and prioritize the development of new features such as dynamic session management for AI, "on-behalf-of" token exchange, and agent-specific audit logs.
• Work closely with engineering and architecture teams to define protocols (e.g., extensions to OAuth/OIDC) and deployment models that align with modern AI orchestration frameworks.
• Drive cross-functional execution with design, engineering, and documentation teams to deliver high-quality features on time.
• Track industry trends in LLMs, agentic frameworks (e.g., LangChain, AutoGPT), and emerging AI security standards to develop differentiated solutions.
• Leverage product data to continually improve agent lifecycle management and drive adoption within the developer community.
• Serve as the subject matter expert for AI Identity, enabling marketing and technical sales teams with the tools needed to position Okta for AI use cases.

Required Experience:

• 5+ years of technical product management experience in enterprise-scale SaaS products, or an equivalent background demonstrating core PM competencies.
• Strong knowledge of Identity and Access Management (IAM) protocols such as OAuth2, OIDC, and SAML.
• Hands-on experience with AI/ML frameworks or building products that leverage Large Language Models (LLMs).
• Analytical and decisive: Ability to drive action even with incomplete or ambiguous information in the rapidly evolving AI landscape.
• Excellent communication skills: Ability to work cross-functionally with research, design, engineering, and customer success teams.
• Technical background: Bachelor’s degree in Computer Science, Computer Engineering, or equivalent experience that allows you to influence technical design decisions.

Preferred Experience:

• Experience with developer-centric products, such as building with SDKs, APIs, or no-code/low-code integration platforms.
• Familiarity with Zero Trust architecture and how it applies to non-human entities and automated workloads.
• Prior experience in a "Zero to One" product environment, launching new products from initial concept to GA.
• Advanced degree in a technical or business field.

Annual Salary Range: $146,000-$200,000 CAD

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Your work will directly shape how our models understand and optimize for what humans actually want , enabling Claude to be more useful, more reliable, and better aligned with human values.

This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking.

You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.

We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources.

Your work will directly advance the science of how we train AI systems to be both highly capable and safe.

Responsibilities:

• Lead research on novel reward model architectures and training approaches for RLHF

• Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability

• Research techniques to detect, characterize, and mitigate reward hacking and specification gaming

• Design experiments to understand reward model generalization, robustness, and failure modes

• Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines

• Contribute to research publications, blog posts, and internal documentation

• Mentor other researchers and help build institutional knowledge around reward modeling

You may be a good fit if you:

• Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning

• Have experience training and evaluating reward models for large language models

• Are comfortable designing and running large-scale experiments with significant computational resources

• Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor

• Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences

• Care deeply about building AI systems that are both highly capable and safe

Strong candidates may also:

• Have published research on reward modeling, preference learning, or RLHF

• Have experience with LLM-as-judge approaches, including calibration and reliability challenges

• Have worked on reward hacking, specification gaming, or related robustness problems

• Have experience with constitutional AI, debate, or other scalable oversight approaches

• Have contributed to production ML systems at scale

• Have familiarity with interpretability techniques as applied to understanding reward model behavior

The annual compensation range for this role is $350,000-$500,000 USD.

Logistics:

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.

How we're different:

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Senior Software Engineer, AI Products (India)

Company Overview

Airbnb is a global online marketplace for booking accommodations, with over 5 million hosts and 2 billion guest arrivals.

The Community You Will Join

The Airfam Products team exists to make every Airbnb employee more productive through a unified digital headquarters experience. As part of a 13-person cross-functional team of engineers, designers, researchers, and product managers, you'll work on platforms that serve Airbnb's entire global workforce. Our portfolio includes One Airbnb (the company's internal cultural hub with enterprise search, people profiles, and AI-powered chat), OneChat (Airbnb's enterprise AI assistant enabling secure LLM interactions), and a suite of tools that power how employees discover information, connect with colleagues, and get work done. You'll be joining the AI for Non-Developers workstream, focused on expanding AI productivity tools to all Airbnb employees,building OneChat Agents, deep research capabilities, artifact creation tools, and task automation that make AI accessible to everyone, regardless of technical background.

The Difference You Will Make

As a Senior Software Engineer on the Airfam Products team, you'll be instrumental in building Airbnb's next generation of AI-powered employee experience platforms. Your work will be a force multiplier for the entire company,every AI feature you ship, every system you architect, and every engineer you mentor will amplify productivity across Airbnb's global workforce. You will:

• Democratize AI by building tools that empower non-technical employees to leverage the power of LLMs
• Drive innovation by taking AI prototypes from concept to production at scale
• Shape the future of how Airbnb employees work, collaborate, and discover information

A Typical Day

• Lead the technical design and implementation of LLM-powered features for OneChat and enterprise AI tools, including RAG pipelines, AI agents, and prompt optimization
• Partner with product managers, designers, and cross-functional teams to translate user problems into AI-powered solutions that serve Airbnb's global workforce
• Develop and iterate on agentic AI capabilities, including multi-step reasoning, tool use, and context-aware decision-making
• Implement evaluation pipelines and quality systems to measure model performance, detect hallucinations, and ensure responsible AI practices
• Own production AI systems end-to-end, including deployment strategies, monitoring, alerting, and incident response
• Collaborate with the DevAI team on AirChat SDK integrations, MCP (Model Context Protocol) implementations, and Glean Action Packs
• Mentor engineers (L6-L8) through design reviews, architecture discussions, and pair programming sessions
• Stay current with the rapidly evolving GenAI landscape, evaluating new models and techniques for potential application
• Balance hands-on technical contributions with technical leadership activities

Your Expertise

• 8+ years of software engineering experience, with significant focus on building production AI/ML systems
• 2+ years of hands-on experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, embeddings, and retrieval-augmented generation (RAG)
• Strong proficiency in backend technologies (TypeScript, Go, or Java)
• Strong backend and distributed systems expertise, including API design (REST, GraphQL) and cloud infrastructure (AWS, GCP, or Azure)
• Track record of shipping AI-powered products from prototype to production
• Proven ability to collaborate cross-functionally and influence without authority
• Excellent communication skills with ability to distill complex technical concepts for diverse audiences
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Preferred

• Master's or PhD in Computer Science, Machine Learning, or related field
• Experience building AI agents and multi-agent systems, preferably using Claude
• Experience building integrations using MCP
• Experience with containerization and orchestration (Docker, Kubernetes)
• Background in building enterprise-grade internal tools and developer productivity platforms
• Experience with frontend technologies (React, Next.js) for full-stack AI product development
• Contributions to open-source Gen AI/ML projects or publications at top venues

Your Location

This position is based in Bangalore, India with a hybrid work arrangement. You'll collaborate with teammates across global time zones, with primary alignment to Pacific Time for key meetings.

Our Commitment to Inclusion & Belonging

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

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Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks.

The ideal candidate will be comfortable working at the intersection of empirical economics, technological change, and policy impact. They will have a strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning.

Some examples of our recent work include:

• Anthropic Economic Index Report: Economic Primitives
• Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption
• Estimating AI productivity gains from Claude conversations
• The Anthropic Economic Index

For this role, we're looking for candidates who can combine rigorous economic analysis with novel measurement approaches to understand AI's transformative effects on the economy.

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In this role, you will have the opportunity to shape our security capabilities from the ground up alongside our world-class research and security teams. You will lead cybersecurity Incident Response efforts covering diverse domains from external attacks to insider threats involving all layers of Anthropic's technology stack.

Key responsibilities include:

• Developing and deploying novel tooling that may leverage Large Language Models to enhance detection, investigation, and response capabilities
• Creating and optimizing detections, playbooks, and workflows to quickly identify and respond to potential incidents
• Reviewing Incident Response metrics and procedures and driving continuous improvement
• Working cross-functionally with other security and engineering teams

Note: This position will require participation in an on-call rotation.

To be successful in this role, you will need:

• 3+ years of software engineering experience, with security experience a plus
• 5+ years of detection engineering, incident response, or threat hunting experience
• A solid understanding of cloud environments and operations
• Experience working with engineering teams in a SaaS environment
• Exceptional communication and collaboration skills
• An ability to lead projects with little guidance
• The ability to pick up new languages and technologies quickly
• Experience handling security incidents and investigating anomalies as part of a team
• Knowledge of EDR, SIEM, SOAR, or related security tools

Strong candidates may also have experience with:

• Performing security operations or investigations involving large-scale Kubernetes environments
• A high level of proficiency in Python and query languages such as SQL
• Analyzing attack behavior and prototyping high-quality detections
• Threat intelligence, malware analysis, infrastructure as code, detection engineering, or forensics
• Contributing to a high-growth startup environment

If you're interested in this role, please submit an application, even if you don't believe you meet every single qualification. We encourage diversity and inclusion in our hiring process.

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The Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. The team builds the training environments that fuel RL at scale. This is a unique role that combines executing directly on ML research, data operations, and project management to improve our models.

Responsibilities:

• Improve and execute our fine-tuning strategies for adapting Claude to new domains and tasks
• Manage technical relationships with external data vendors, including evaluation of data quality and reward design
• Collaborate with domain experts to design data pipelines and evaluations
• Explore novel ways of creating RL environments for high value tasks
• Develop and improve QA frameworks to catch reward hacking and ensure environment quality
• Partner with other RL research teams and product teams to translate capability goals into training environments and evals

You may be a good fit if you:

• Have experience with fine-tuning large language models for specific domains or real-world use cases and/or domain expertise in an area where we would like to make our models more useful.
• Have experience with reinforcement learning, reward design, or training data curation for LLMs
• Are comfortable managing technical vendor relationships and iterating quickly on feedback
• Find value in reading through datasets to understand them and spot issues
• Have strong project management and interpersonal skills
• Are passionate about making AI more useful and accessible across different industries
• Are excited about a role that includes a combination of ML research, data operations, and project management

Strong candidates may also:

• Have experience training production ML systems
• Be familiar with distributed systems and cloud infrastructure
• Have domain expertise in an area where we would like to make our models more useful
• Have experience working with external vendors or technical partners

The annual compensation range for this role is $350,000-$850,000 USD.

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As a Staff Engineer, AI Security on the AppSec team, you'll lead autonomous defense for the AI lifecycle. Build multi-agent frameworks and secure gateways while integrating real-time security gates and identity standards. By mentoring Security and R&D to define the MLSecOps roadmap, you'll ensure a 'secure-by-default' future for agentic workflows and resilient AI innovation.

Responsibilities:

Serve as the primary subject matter expert for all AI and machine learning security initiatives across security and R&D.

Design and manage AI gateways to provide a centralized control plane for authentication and authorization and rate limiting across all model and tool interactions.

Build and maintain an autonomous security agentic framework that utilizes multi agent orchestration for end to end investigation and alert triage and remediation.

Develop agentic identity models using OAuth 2.1 to propagate identity across trust boundaries and prevent the confused deputy problem.

Help govern the AI augmented software development lifecycle by integrating real time security gates into the developer environment and CI/CD pipeline.

Manage Agentic Security Solutions that secure AI lifecycle and manage AI workloads at runtime.

Author company wide AI security standards and implement these security checks across Twilio's stack.

Implement human in the loop checkpoints and transactional safety protocols for high impact or destructive agentic actions.

Partner with engineering leadership to set the long term roadmap for identity centric security and automated posture management.

Act as a knowledge multiplier by mentoring security engineers and developing secure by default paved road templates for R&D teams

Qualifications:

8+ years of experience in security engineering with at least 3 years focused on AI or machine learning security operations (MLSecOps).

Expertise in orchestrating multi-agent systems with AWS Strands, LangGraph, and CrewAI, specializing in runtime isolation, PII redaction, and defending against indirect prompt injection in agentic environments.

Hands-on experience with AI-specific frameworks (e.g., MITRE ATLAS, MAESTRO, OWASP Top 10 for LLMs/Agents/MCP) to threat model and defend against a wide spectrum of risks, including direct/indirect prompt injection, training data poisoning, tool poisoning, and data exfiltration within agentic workflows.

Proficiency in securing end-to-end AI pipelines, from data ingestion and training to model deployment and monitoring.

Strong communication skills to translate complex AI risks into actionable business logic for stakeholders.

Desired:

Hands-on experience in modern application security tooling including SAST and SCA and DAST with experience adapting these tools to catch AI specific vulnerabilities like indirect prompt injection.

Expertise in identity standards including OAuth 2.1 and PKCE.

Experience with AI Red Teaming and conducting adversarial simulations against Large Language Models (LLMs) and agentic systems.

Proficiency in at least one general programming language (Python, Go, etc) with experience in container security and workload isolation.

Proven ability to operate with autonomy and drive high impact outcomes in ambiguous environments by identifying and executing on critical projects without predefined roadmaps or direct supervision.

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As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

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You'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We conduct all interviews in Python, and this role may require responding to incidents on short-notice, including on weekends.

Responsibilities:

Implement and optimize post-training techniques at scale on frontier models

Conduct research to develop and optimize post-training recipes that directly improve production model quality

Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation

Develop tools to measure and improve model performance across various dimensions

Collaborate with research teams to translate emerging techniques into production-ready implementations

Debug complex issues in training pipelines and model behavior

Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities

Adapt quickly to changing priorities

Maintain clarity when debugging complex, time-sensitive issues

Have strong software engineering skills with experience building complex ML systems

Are comfortable working with large-scale distributed systems and high-performance computing

Have experience with training, fine-tuning, or evaluating large language models

Can balance research exploration with engineering rigor and operational reliability

Are adept at analyzing and debugging model training processes

Enjoy collaborating across research and engineering disciplines

Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

Have experience with LLMs

Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.

However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.

The annual compensation range for this role is $350,000-$500,000 USD.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the role

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

Responsibilities

• Implement and optimize post-training techniques at scale on frontier models
• Conduct research to develop and optimize post-training recipes that directly improve production model quality
• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
• Develop tools to measure and improve model performance across various dimensions
• Collaborate with research teams to translate emerging techniques into production-ready implementations
• Debug complex issues in training pipelines and model behavior
• Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
• Adapt quickly to changing priorities
• Maintain clarity when debugging complex, time-sensitive issues
• Have strong software engineering skills with experience building complex ML systems
• Are comfortable working with large-scale distributed systems and high-performance computing
• Have experience with training, fine-tuning, or evaluating large language models
• Can balance research exploration with engineering rigor and operational reliability
• Are adept at analyzing and debugging model training processes
• Enjoy collaborating across research and engineering disciplines
• Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

• Have experience with LLMs
• Have a keen interest in AI safety and responsible deployment

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high-impact real-world deployments.

Responsibilities:

• Develop and train audio models, including conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, and generative audio models
• Work across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization
• Collaborate with teams across the company to develop and deploy audio technologies
• Communicate clearly and effectively with colleagues and stakeholders

Strong candidates may also have experience with:

• Large language model pretraining and finetuning
• Training diffusion models for image and audio generation
• Reinforcement learning for large language models and diffusion models
• End-to-end system optimization, from performance benchmarking to kernel optimization
• GPUs, Kubernetes, PyTorch, or distributed training infrastructure

Representative projects:

• Training state-of-the-art neural audio codecs for 48 kHz stereo audio
• Developing novel algorithms for diffusion pretraining and reinforcement learning
• Scaling audio datasets to millions of hours of high-quality audio
• Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness
• Studying training dynamics of mixed audio-text language models
• Optimizing latency and inference throughput for deployed streaming audio systems

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As a research engineer, you'll partner with the product org to ensure that the vision improvements you deliver impact Claude's performance on real-world tasks. You'll also work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning.

Strong candidates may also have experience with large-scale pretraining, SL, and RL on language models, deep learning research on images, video, or other modalities, developing complex agentic systems using LLMs, high-performance ML systems (GPUs, TPUs, JAX, PyTorch), and large-scale ETL and data pipeline development.

The annual compensation range for this role is $350,000-$850,000 USD.

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Responsibilities:

• Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process
• Develop privacy-first training algorithms and techniques
• Develop evaluation and auditing techniques to measure the privacy of training algorithms
• Advocate on behalf of our users to ensure responsible handling of all data

You may be a good fit if you have:

• Experience working on privacy-preserving machine learning
• A track record of shipping products and features inside a fast-moving environment
• Strong coding skills in Python and familiarity with ML frameworks like PyTorch or JAX.
• Deep familiarity with large language models, how they work, and how they are trained
• Have experience working with privacy-preserving techniques (e.g., differential privacy and how it is different from k-anonymity, l-diversity, and t-closeness)
• Experience supporting fast-paced startup engineering teams
• Demonstrated success in bringing clarity and ownership to ambiguous technical problems
• Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics

Strong candidates may also:

• Have published papers on the topic of privacy-preserving ML at top academic venues
• Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models)
• Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus)

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Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We are hiring security specialists who are experienced at exploitation and remediation, and are interested in understanding how LLMs could cause harm in the future, so that we can better prepare for this future and mitigate these risks before they arise.

Responsibilities:

• Triage any vulnerabilities discovered, coordinate and assist the external and open-source community in remediation
• Write scaffolds designed to automate typical traditional attack techniques to help clarify our defensive problem selection
• Research how adversaries might misuse LLMs to identify and exploit vulnerabilities at scale in the future
• Develop promising defensive strategies that could mitigate the ability of adversaries to misuse models in harmful ways
• Work with a small, senior team of engineers and researchers to enact a forward-looking security plan

You may be a good fit if you have:

• 3+ years experience with pentesting, vulnerability research, or other offensive security experience
• Senior-level knowledge in at least one related topic area (reverse engineering, network security, exploitation, physical security)
• A history demonstrating desire to do the 'dirty work' that results in high-quality outputs
• Software engineering experience
• Demonstrated success in bringing clarity and ownership to ambiguous technical problems
• Proven ability to lead cross-functional security initiatives and navigate complex organisational dynamics

Strong candidates may also have:

• Published research papers on computer security, language modeling, or related topics; or given talks at Defcon, Blackhat, CCC, or related venues
• Familiarity with large language models and how they work; for example, you may have written agent scaffolds
• Reported CVEs, or been awarded for bug bounty vulnerabilities
• Contributed to open-source projects in LLM- or security-adjacent repositories

The annual compensation range for this role is $320,000-$405,000 USD.

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Head of Solutions Architects, Applied AI (Korea)

About Anthropic

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. The company is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

As the founding leader of Applied AI Solutions Architecture in Korea, you will drive the adoption of frontier AI by enabling the deployment of Anthropic's products (Claude for Enterprise, Claude Code, and API) across Korean enterprises and digital-first organisations. You'll leverage your technical skills and consultative sales experience to drive positive AI transformation that addresses our customers' business needs, meets their technical requirements, and provides a high degree of reliability and safety.

Responsibilities

• Build and manage the foundational team of Applied AI professionals in Seoul (Solutions Architects and Product Engineers) providing both technical guidance and career development
• Set goals and reviews for your team, promoting growth and output
• Work with a handful of highest-value enterprise customers on their overall AI adoption strategies, focusing on pre-sales technical excellence including use case scoping, technical champion building, and POC execution
• Partner closely with your aligned GTM leadership to understand customer requirements & co-build GTM strategies to drive adoption for Korean enterprise customers
• Contribute to thought leadership through conference presentations, webinars, and technical content creation
• Own the technical portions of pre-sales engagements, ensuring your team provides compelling demos and validates enterprise customer ROI from Anthropic products
• Drive collaboration from cross-functional teams to influence and unify stakeholders at all levels of the organisation to drive business outcomes
• Travel regularly to customer sites for executive-level sessions, technical workshops, and building relationships
• Establish a shared vision for creating solutions that enable beneficial and safe AI in technology products
• Lead the vision, strategy, and execution of innovative solutions that leverage our latest models' capabilities
• Stay current with emerging AI/ML trends and competitive landscape in the Korean enterprise tech sector

Requirements

• 7+ years of experience as a Solutions Architect, Sales Engineer, or similar pre-sales technical role
• 3+ years of technical go-to-market management experience, specifically managing pre-sales teams
• Native or business-level fluency in Korean and professional proficiency in English
• Experience working with Korean enterprise customers and understanding local business culture and decision-making processes
• Experience with the unique technical requirements and technical procurement process of enterprise tech companies
• Deep technical proficiency with enterprise AI deployments, API integrations, and production LLM use cases
• Have an organisational mindset and enjoy building foundational teams in a relatively unstructured environment
• Have excellent communication, collaboration, and coaching abilities
• Are comfortable dealing with highly uncertain, ambiguous, and fast-moving environments typical of the tech industry
• Strong executive presence and ability to foster deep relationships with technical leaders and engineering teams
• Have at least a high level familiarity with the architecture and operation of large language models and/or ML in general
• Experience with prompt engineering, LLM evaluation, and architecting AI-powered systems
• Make ambiguous problems clear and identify core principles that can translate across scenarios
• Have a passion for making powerful technology safe and societally beneficial
• Think creatively about the risks and benefits of new technologies, and think beyond past checklists and playbooks
• Stay up-to-date and informed by taking an active interest in emerging research and industry trends
• Understanding of developer tooling, SDKs, and technical integration patterns common in enterprise tech companies

Benefits

• Competitive salary and benefits package
• Opportunity to work with a talented and diverse team
• Professional development and growth opportunities
• Flexible work arrangements

How to Apply

If you're interested in this opportunity, please submit your resume and a cover letter explaining why you're a great fit for this role. We can't wait to hear from you!

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This role sits at the intersection of technical leadership, customer engagement, product strategy, and internal enablement. You'll work directly with CIOs, CTOs, Chief Underwriting Officers, Chief Claims Officers, and technology leaders at major insurers and brokers to design solutions that address their most critical challenges,from underwriting automation and claims processing to fraud detection and customer experience. Your insights will flow directly to Product and Research, shaping Claude's roadmap for insurance. Equally important, you'll enable Anthropic's GTM teams with deep domain expertise, thought leadership content, and strategic guidance.

With AI poised to transform insurance operations and the industry seeking partners who understand their unique challenges, you'll play a pivotal role in establishing Anthropic as the AI platform of choice for insurers worldwide.

Responsibilities:

• Technical Evangelism & Thought Leadership: Represent Anthropic's technical vision to the insurance industry through conference keynotes, executive briefings, whitepapers, and industry advisory boards. Establish Claude as the leading AI platform for P&C, Life, and Reinsurance markets.

• Strategic Customer Engagement: Serve as executive technical sponsor for strategic insurance accounts, engaging CIOs, CTOs, Chief Underwriting Officers, Chief Claims Officers, and Heads of AI/ML on enterprise AI strategy. Build peer-to-peer relationships that position Anthropic as a strategic transformation partner.

• Product Partnership & Roadmap Influence: Translate customer requirements and competitive dynamics into product priorities. Partner closely with Product and Research to develop insurance-specific capabilities including model governance, explainability, audit logging, and regulatory compliance features.

• Pricing & Commercial Strategy: Contribute to pricing and packaging decisions with insurance market insight. Understand value drivers for different insurance segments, competitive positioning, and deal structuring that aligns with how insurers buy technology.

• Internal Enablement & Advocacy: Train and uplift Anthropic's GTM teams on insurance domain expertise, use cases, and buyer personas. Create enablement content, conduct training sessions, and serve as the internal authority on all things insurance. Influence without authority across the organisation.

• Regulatory & Compliance Navigation: Partner internally and externally to develop thought leadership regarding AI governance frameworks aligned with state insurance regulations, NAIC guidelines, and international requirements. Help customers understand model risk management, explainability requirements, and responsible AI practices for insurance.

• Ecosystem Partnership: Build technical relationships with GSIs (Deloitte, Accenture, McKinsey), cloud providers (AWS, Azure, GCP), and insurance technology vendors (Guidewire, Duck Creek, Majesco).

Requirements:

• Have 20+ years of experience in technology and/or business roles with at least 8 years in senior leadership positions (CTO, VP Engineering, Chief Architect, Chief Underwriting Officer, Head of Claims Technology, Distinguished Engineer) in insurance

• Have deep domain expertise across multiple insurance segments,P&C (personal and commercial lines), Life & Annuities, or Reinsurance,with hands-on experience building or operating mission-critical systems

• Have a strong understanding of AI/ML technologies including large language models, with the ability to engage credibly on technical architecture, model behaviour, and system design

• Have established executive networks across insurance technology leadership (CIOs, CTOs, Chief Underwriting Officers) and credibility as a thought leader in the industry

• Have deep knowledge of insurance regulatory requirements (state insurance regulations, NAIC model laws, Solvency II, Lloyd's requirements) and experience implementing compliant technology solutions

• Are skilled at translating complex technical concepts into business value propositions that resonate with both technical and business stakeholders

• Have experience influencing product roadmaps based on customer feedback and market requirements, working effectively with Product and Engineering teams

• Are passionate about responsible AI development and Anthropic's mission, with a commitment to helping insurers adopt AI safely and beneficially

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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Responsibilities:

• Conduct research on post-training methods for information-seeking scenarios in Gemini, including reinforcement learning and self-supervised training.
• Develop novel evaluation methods for improving model quality, grounding, and factuality.
• Investigate orchestration of tool calls and improved retrieval methods for information-seeking scenarios.

Requirements:

• PhD in a relevant area, or an equivalent research/publication record.
• Strong software-engineering skills in addition to a research background.

Preferred Qualifications:

• Experience in reinforcement learning.
• Experience in post-training methods.
• Experience in Large Language Models for information-seeking scenarios.

The US base salary range for this full-time position is between $147,000 USD - 211,000 + bonus + equity + benefits.

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Research Scientist, Information Quality

Job Description

This role requires a passion for advancing information literacy through AI & machine learning, focusing on assessing media trustworthiness (images, audio, and video) and exploring concepts like authenticity, provenance, and context.

Key responsibilities include formulating metrics, simulations, rapid prototyping of ML techniques, exploratory data analysis, collaborating with product teams to drive research, and developing tools and frameworks to accelerate research. A public example of research work is Backstory.

About Us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.

The Role

To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers, and engineers to conduct cutting-edge research and advance the next generation of multimodal AI assistants that help co-investigation and deliberation.

Relevant domains may include, but are not limited to, determining media authenticity, context discovery, and open source intelligence investigations. A public example of recent work is Backstory.

Key responsibilities:

• Drive the projects by defining key research questions.
• Design, implement, and evaluate experiments to provide clear answers
• Contribute to real world impact, by landing your research in Google products and services.
• Publish research findings in top academic conferences and journals
• Stay up-to-date with the latest advancements in the field
• Collaborate with internal and external scientific domain experts.

About You

In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

• PhD in Computer Science, Statistics, or a related field.
• Strong publication record in top machine learning and/or computer vision conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).
• Expertise in one or more of the following areas: social impact of AI, reinforcement learning, multimodal agents, computer vision, natural language understanding.

In addition, the following would be an advantage:

• Passion for research on societal benefits and implications of the internet and AI with focus in information literacy.
• Experience with training, evaluating, and interpreting large language models.
• Experience working with large and noisy datasets.
• Experience collaborating across fields.
• Proven ability to design and execute independent research projects.

When assessing technical background we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect you to be an expert in all fields simultaneously. At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact.

We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law.

If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.

Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

Application deadline: April 28th, 2026

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As a Senior Technical Program Manager in the GeminiApp team, you will drive our organisation-wide and cross-product area initiatives that change the trajectory of our business and product.

Key Responsibilities

• Translate ambiguous, open-ended business priorities into structured and executable programs.
• Proactively identify and mitigate risks, implementing solutions to keep projects on velocity.
• Mechanize critical parts of the business to generate predictable outputs.
• Spearhead critical programs, driving progress from conception to delivery.
• Forge close partnerships with PM and Engineering leads to define product strategy and ensure precise execution.
• Navigate and resolve complex dependencies across diverse workstreams, functions, and organisations.
• Deliver clear, consistent updates on progress, risks, and plans to senior leadership.
• Excel at managing multiple, time-sensitive projects concurrently.
• Guide cross-functional teams in identifying, prioritising, and tracking tasks to meet key deadlines.

Requirements

• Bachelor's degree in a technical field, or equivalent practical experience.
• 10+ years of experience in technical program management or engineering management.
• Track record of disambiguating and delivering repeatable business value.
• Track record of fast-paced, high-volume, and cutting-edge consumer software.
• Proven track record of collaborating with and influencing stakeholders across various functions and sites, especially in situations with little authority and considerable ambiguity.
• Exceptional communication, writing, and presentation skills.

Preferred Qualifications

• Experience working delivering products that leverage large language models as part of primary experience.
• Experience in hardware and software development life-cycles, metric definition and executive presentation, and technical program management.
• Extensive experience in capacity and resource planning.
• Experience in implementing process improvement methodologies to drive efficiency and scale across multiple teams.
• Experience with motivating teams and individuals to change as the business environment changes.
• Ability to lead and inspire geographically dispersed teams, build strong relationships, and collaborate with stakeholders at all levels.
• Excellent strategic thinking and planning skills, with the ability to translate business objectives into initiatives and execute on them.

Salary

The US base salary range for this full-time position is between $227,000 USD - 320,000 USD + bonus + equity + benefits.

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Responsibilities:

• Serve as a technical advisor to Anthropic customers as they deploy new products & workflows with our models: from discovery through deployment, coordinating internally across multiple teams to drive customer success
• Partner with account executives to deeply understand customer product requirements and architect technical solutions, ensuring alignment between business objectives and technical implementation
• Guide technical architecture decisions and help customers build state-of-the-art products & workflows with LLMs via API
• Develop customized pilots, prototypes, and evaluation suites that make the case for customer deployment of our models into customer products and workflows via our API
• Lead hands-on technical workshops and code reviews with customer engineering teams
• Identify common design patterns and contribute insights back to our Product and Engineering teams
• Maintain strong knowledge of the latest developments in LLM capabilities, implementation patterns, and AI product development stacks
• Travel occasionally to customer sites for workshops, implementation support, and building relationships
• Attend conferences, lead speaking engagements, write blog posts and white papers on topics surrounding the AI space

Requirements:

• 4+ years of experience in a technical role such as Customer Engineer, Forward Deployed Engineer, Software Engineer or Technical Product Manager with a desire to work closely with customers
• Production experience with LLMs including advanced prompt engineering, agent development, evaluation frameworks, and deployment at scale
• Strong programming skills with proficiency in Python and experience building production applications
• Expertise working with common LLM implementation patterns, prompt engineering, evaluation frameworks, agent frameworks, and retrieval frameworks.
• Ability to navigate ambiguity and execute across domains with intellectual openness, finding simple solutions to complex problems
• High cooperation mindset for cross-organizational collaboration, balancing competing priorities with integrity
• Passion for advancing safe, beneficial AI systems through creative technical applications
• Exceptional communication skills to convey technical concepts to diverse stakeholders while maintaining a low ego and collaborative approach

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