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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This role requires 5+ years of experience in chief-of-staff, program management, operations, or similar roles in a research, technical, or fast-moving environment.

Key responsibilities include:

• Scope, plan, and drive model evaluation projects end-to-end
• Manage external research collaborators
• Synthesize complex information into decision-relevant inputs for leadership
• Identify new projects the company should take on
• Run the Alignment team offsite and similar events

Strong candidates may also have experience working directly with researchers, especially in AI safety or machine learning, and familiarity with the AI safety research landscape, key organizations, and ongoing debates.

Annual compensation range: $210,000-$290,000 USD.

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Key responsibilities include:

• Owning the creation of model evaluation from initial hypothesis, data scraping to final publication.
• Deeply analysing why the model failed and identifying semantic patterns, edge cases, and systemic hallucinations in raw model outputs.
• Reviewing raw data sets, meeting transcripts, and research notes to identify the 'so what' and turning these findings into a logical hierarchy.
• Acting as the bridge between the data and the narrative by structuring findings into a logical hierarchy where the most critical 'hook' lands first, followed by the supporting evidence.

As a successful candidate, you will have 5-10 years of experience in DS, ML, AI research and analysis, be a structured thinker, have a high tolerance for ambiguity, and possess executive presence. Experience in Model Evaluation, ML Engineering or Technical Research is a plus.

This role offers a competitive compensation package, including base salary, equity, and benefits. The base salary range for this full-time position in San Francisco is $205,600-$257,000 USD.

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Your North Star: Build innovative, data-driven products that enhance patient engagement and retention to improve the lives of moms and babies.

We're looking to bring on an experienced Senior Data Scientist to own the data strategy for our Growth and Patient Experience team. In this role, you will hold a dual focus: optimizing the top-of-funnel strategy to grow patient enrollment, and driving the development of next-generation products that keep those patients engaged throughout their care journey.

Responsibilities

• Become an expert in patient behavior, engagement patterns, and retention drivers, owning the team’s data science roadmap and playing a foundational role in shaping Pomelo's patient experience strategy
• Collaborate deeply with the Marketing team to build and optimize a multi-channel enrollment strategy. You will build attribution models, analyze funnel conversion rates, and identify the best ways to allocate resources towards patient enrollment.
• Define and iterate on key metrics that define the health of the business, such as enrollment and retention rates, and present insights to Pomelo’s leadership team
• Complete exploratory analyses to inform development of enrollment and engagement-focused features, such as conversation topic categorization and patient journey segmentations
• Build robust data pipelines and analytics infrastructure that connect external, marketing and product usage data, creating a unified view of the patient journey.
• Lead the team’s approach to A/B testing across marketing and product initiatives to accurately measure impact
• Collaborate closely with Product, Clinical, and Engineering teams to translate patient insights into actionable product improvements and new feature development

Requirements

• 4+ years professional experience in data science, product analytics, or data engineering
• 1+ years professional experience working in healthcare, consumer health, or high-touch consumer products
• Expert proficiency in both SQL and Python, with demonstrated ability to work across the full data stack
• Strong product sense with experience in product prototyping, A/B testing, and user behavior analysis
• Passionate about improving maternal health outcomes and creating exceptional patient experiences
• Proven track record of scoping and implementing data-driven product solutions to complex, ambiguous problems
• Comfortable leading cross-functional initiatives with Product, Engineering, and Clinical teams as a strategic partner and technical expert
• A strong communicator and a keen nose for value - someone who can easily and quickly understand the needs of different audiences
• Experience with data visualization and dashboarding tools (e.g., Looker, Tableau, Metabase)
• Experience working with modern data stack tools (e.g., dbt, Airflow, Snowflake)

Bonus Points

• Experience with AI model development including fine-tuning, prompt engineering, model evaluation and building production AI applications
• Background in healthcare AI, clinical decision support, or medical NLP

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Key responsibilities include:

• Developing and deploying ML-based search and discovery relevance models and systems integrated with Databricks' products and services.
• Designing and implementing automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration.
• Collaborating with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
• Contributing to building a robust framework for evaluating search ranking improvements - both offline and online.

Requirements include:

• A Bachelor's degree in Computer Science or a related field, with a Master's or PhD preferred.
• 5+ years of experience developing search relevance systems at scale in production or in high-impact research environments.
• Experience applying LLM to search relevance.
• Strong understanding of computer science fundamentals.
• Contributions to well-used open-source projects.

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As a senior machine learning engineer, you will play a key role in the end-to-end design and implementation of our product, which is a platform for powering use cases across training and serving of generative AI models. You will work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform.

Key responsibilities include:

• Designing and building the core platform infrastructure that supports our customer-facing product features
• Ensuring the reliability, security, and scalability of the backend distributed systems that power all aspects of our product
• Translating product requirements into user interfaces and backend distributed system design and owning end-to-end implementation

We look for:

• 4+ years of hands-on programming experience with at least one modern language such as Python, Scala, Go, or C++
• Strong sense of distributed systems design and experience building large-scale systems
• Experience building ML platform systems for applications in the ML model development lifecycle such as data preparation, model training, model evaluation, and model serving
• Direct experience developing ML models is a plus but not required
• Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability
• Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

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We're hiring Engineering Managers to lead the teams building Claude for Financial Services, Life Sciences, Healthcare, and Legal. You'll lead a team shipping AI into professional workflows,owning execution, working directly with customers and go-to-market, and helping shape where the broader Verticals group goes next.

Responsibilities

• Lead and develop a team of engineers building new AI products for enterprise customers in your vertical
• Work closely with research to make the models better in your domain,shaping evals, surfacing failure modes, and feeding customer learnings back into model development
• Own engineering execution end-to-end: planning, prioritization, delivery quality, team health, and incident response
• Partner with sales and customer success on enterprise deals,understanding requirements, joining key conversations, and turning what you learn into engineering priorities
• Shape the roadmap with product and design, not just execute against it
• Drive the compliance and platform-readiness work your customers require, partnering with security and legal
• Recruit, onboard, and grow strong engineers; give direct feedback and build a healthy, high-performing team

You may be a good fit if you

• Have built AI products and have a practical understanding of what it takes to turn model capabilities into applications people actually use
• Are comfortable working with enterprise customers, working alongside sales and customer success and joining customer conversations
• Know the operational realities of building on platforms and integrations you don't control
• Are a skilled engineering manager who treats management as a craft,clear feedback, strong 1:1s, consistent investment in your team's growth

Strong candidates may also have

• Experience in working with research to improve domain specific model capabilities
• Experience with model evaluation frameworks and how evals inform product decisions
• Experience taking a product from 0→1,forming a team, finding product-market fit, and shipping the first version with limited precedent to lean on
• Deep domain knowledge in one of these verticals,investment banking, asset management, insurance, or corporate finance; drug discovery or computational biology; clinical operations, health systems, or payers; or legal practice or legal tech
• Direct experience with the compliance frameworks relevant to these industries, including owning that work within an engineering org
• Exposure to both product-led growth and direct enterprise sales, and an understanding of how engineering decisions interact with each

Representative projects

• Partnering with an enterprise customer to map a core workflow,a deal-documentation process, a target-identification pipeline, a prior-authorization queue,and turning it into an engineering roadmap with a new or existing AI product
• Collaborating with research to design an evaluation framework that gives reliable signal on output quality across your domain's use cases
• Owning a platform-readiness initiative end-to-end,scoping with legal and security, defining the engineering work, and shipping it

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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As a Model Behavior Architect at Mistral AI, you will be at the forefront of defining and measuring Large Language Model (LLM) behavior. You will work closely with our Science team to define what 'good' looks like for various tasks, including Reasoning, Audio, Alignment, Tools, and Frontier bets.

Responsibilities

• Interact with models to identify areas for improvement in model behavior
• Gather internal and external feedback on model behavior to scope areas for improvement
• Design and implement evaluation pipelines, data guidelines, data generation, and synthetic testing environments
• Identify and fix edge case behaviors through rigorous testing
• Develop robust evaluation pipelines for model candidates
• Collaborate with AI Scientists

About you

• You have a deep understanding of linguistics, language, and translation, engineering and code behavior, or LLM agents at work, including reasoning and tool use
• You have prior knowledge in training and optimizing model behavior
• You are an expert at building robust evaluations
• You thrive in dynamic and technically complex environments
• You have a track record of delivering innovative, out-of-the-box solutions to address real-world constraints

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The Subscriptions Mission builds and evolves Spotify's subscription products and marketplace experiences to drive sustainable user and revenue growth globally. We focus on awareness, acquisition, activation, retention, and monetization strategies that help users unlock the full value of Spotify while enabling the business to scale efficiently and responsibly.

User Understanding sits within Subscriptions and focuses on building the intelligence layer that makes our growth efforts smarter,through decisioning, data signals, and ML models that power personalization across surfaces and lifecycle moments. You'll work at the intersection of machine learning innovation and commercial strategy, partnering closely with ML engineers, data scientists, data platform teams, product insights, and business stakeholders to shape high-impact growth initiatives.

Responsibilities

• Define the strategy and roadmap for key subscriber growth initiatives, focusing on applying AI/ML in the right places balancing business goals, ROI and technical feasibility
• Deeply understand business metrics and user behaviour, leading to well-researched hypotheses for testing and future AI/ML development
• Lead end-to-end product development from problem definition through system deployment and adoption, working across research, engineering, and business teams
• Drive experimentation programs that rigorously test ML-powered features against baselines, making data-informed decisions about when to scale AI-driven experiences
• Collaborate with engineers and data scientists on technical decisions to drive innovation and impact
• Build products at scale that impact hundreds of millions of users across diverse markets, considering localization, infrastructure constraints, and varying user contexts
• Stay current with AI and ML technology trends, looking for opportunities to match with key business objectives

Requirements

• 5+ years product management experience with proven track record launching ML/data-driven products at scale
• Strong technical fluency in data science, recommender systems, machine learning concepts, model evaluation, A/B testing, and data pipelines. You can review and challenge technical decisions.
• Strategic judgment about when to apply ML vs. GenAI vs. other tools and you understand the cost-benefit tradeoffs of complex systems
• Understand subscription business and e-commerce fundamentals in consumer products
• Excellent communication skills, able to collaborate cross-functionally and communicate complex ideas to stakeholders at all levels

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At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.

We are a global company with teams distributed between France, USA, UK, Germany and Singapore. We are a diverse workforce that thrives in competitive environments and is committed to driving innovation.

Why This Role Matters

You will be the technical linchpin between Mistral and our strategic partners in ASEAN (Nvidia, Dell, Hyperscalers, Global System Integrators), translating our open-weight models and sovereign AI architecture into deployable, scalable solutions.

By designing joint architectures, influencing partner GTM motions, and earning a seat at the CIO/CTO table, you will accelerate Mistral’s technical credibility and deployment velocity across Asia Pacific.

This is a foundational role where you will define how open-weight AI is operationalized at scale in the region.

What You Will Do

Partner Technical Leadership & Architecture Design

• Lead the technical design, deployment, and enablement of Mistral’s partner solutions, bridging our AI models with partner infrastructure (Nvidia, Dell, Hyperscalers, GSIs) to deliver scalable AI Labs, AI Factories, and sovereign AI architectures.

• Serve as the trusted technical advisor to partner CTOs, CIOs, and engineering leaders—shaping joint architectures, guiding GPU/model deployment strategies, and accelerating GTM execution.

• Design reference architectures and deployment patterns for partner-led implementations (e.g., multi-GPU inference clusters, AI Lab topologies, private AI clouds).

• Innovate the Executive Briefing Center (EBC) function for technical leaders (CIOs, CTOs, CDOs), positioning Mistral as the default choice for enterprise AI.

• Co-design sovereign AI reference architectures with Nvidia and Dell (H100, H200, GB200 platforms).

Co-Sell & Revenue Enablement

• Collaborate with Mistral’s partner and sales teams to progress deals, providing technical expertise to penetrate accounts and influence GTM pipeline.

• Support partners in qualifying/disqualifying opportunities, ensuring Mistral solutions unlock maximum value for customers.

• Deploy Mistral’s enterprise AI suite (models, fine-tuning, use-case building) in partner-led environments, tailoring solutions to customer requirements.

Trusted Advisor & Lighthouse Implementations

• Drive strategic partner-led opportunities through technical discovery, architecture design, and POC execution.

• Lead lighthouse deployments that become referenceable case studies (e.g., Singtel AI Grid, Accenture AI Lab).

• Establish a scalable partner enablement framework, training 100+ partner engineers across ASEAN.

Product Feedback & Internal Collaboration

• Coordinate with Mistral’s product and engineering teams to relay partner-specific requirements and feedback.

• Align joint GTM and technical execution between Mistral Science, Partner Engineering, and partner field teams.

About You

Must-Have

• 10–15 years’ experience in partner-facing technical sales or solution architecture (e.g., Partner SA, Alliance Architect, Partner Technology Strategist).

• Proven ability to engage C-suite and senior technical stakeholders (CTO, CIO, Chief Architect) in strategic architecture discussions.

• Deep GenAI/LLM expertise: RAG, fine-tuning, prompt engineering, model evaluation, and deployment patterns.

• Technical mastery of AI/ML infrastructure (GPU clusters, cloud platforms, model deployment frameworks).

• Track record of co-designing/deploying joint solutions with ecosystem partners (Nvidia, Dell, AWS, Accenture, etc.).

• Executive communication: Ability to articulate science-driven value propositions to technical and business audiences.

• Entrepreneurial mindset: Operates autonomously in high-growth environments; creates playbooks, not follows them.

• Fluent in English; confident working across diverse, cross-cultural teams in Asia.

Nice-to-Have

• Experience with open-weight LLMs or open-source AI stacks (Mistral, Hugging Face, LangChain, vLLM, RAG frameworks).

• Prior involvement in AI Lab, AI Factory, or Sovereign Cloud deployments.

• Familiarity with data governance, model evaluation, and GPU sizing for large-scale inference.

• Network across GSIs and infrastructure partners in Asia

• Exposure to multi-region partner programs or joint GTM initiatives in APJ.

• Bonus languages: Korean, Japanese, or Mandarin for regional partner engagement.

What we offer

• 💰 Competitive cash salary and equity

• 🚑 Health Insurance : Best in Class

• 🥎 Sport : $90 for gym membership allowance

• 🥕 Food : $200 monthly allowance for meals (solution might evolve as we grow bigger)

• 🚴 Transportation : $120/month for public transport or Parking charges reimbursed

• 🏝️ PTO: 18 per year

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As a Senior Product Manager, you will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers. You will develop and execute on LLM platform strategy for Copilot that extend language model's capabilities. You will prototype approaches by steering language models to drive response quality across a wide range of scenarios. You will identify and prioritise platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution.

You will define and build measurable evaluations with relevant datasets to demonstrate quality improvements. You will define, deploy and manage experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users. You will partner with product teams to scale tool building and work with inference, agents and orchestration teams to resolve dependencies. You will be accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery.

Responsibilities include:

• Developing and executing on LLM platform strategy for Copilot that extend language model's capabilities
• Prototyping approaches by steering language models to drive response quality across a wide range of scenarios
• Identifying and prioritising platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution
• Defining and building measurable evaluations with relevant datasets to demonstrate quality improvements
• Defining, deploying and managing experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users
• Partnering with product teams to scale tool building and working with inference, agents and orchestration teams to resolve dependencies
• Being accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery

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Financial services is one of the most consequential domains for AI—and one of the most demanding. Workflows are complex, data is sensitive, regulatory expectations are real, and the people using these tools are experts who will immediately know if something doesn't work. That's exactly the kind of challenge we're here for.

Anthropic's Verticals team builds AI-powered products purpose-built for the industries where this complexity is highest. We're in early growth, moving fast, and earning the trust of enterprise customers who have seen a lot of vendors promise transformation and deliver demos. This role is about building something that actually changes how financial services teams work—and building the team that can do that sustainably.

Responsibilities

• Lead and develop a team of engineers building AI-powered experiences in Excel and PowerPoint for financial services enterprise customers

• Own engineering execution end-to-end: project planning, prioritization, delivery quality, team health, and incident response

• Partner closely with sales and customer success teams on enterprise deals—understanding customer requirements, participating in key conversations, and translating what you learn into engineering priorities

• Work with product and design to shape the roadmap, not just execute against it; you'll have a meaningful voice in what we build and why

• Maintain operational reliability on top of third-party platforms (the Microsoft 365 ecosystem), and build the processes that make your team resilient when those dependencies behave unexpectedly

• Engage with research and evaluation frameworks—developing intuition for model behavior, understanding evals, and helping the team make sound tradeoffs between capability and reliability

• Drive compliance and regulatory initiatives relevant to your customers, including owning the internal engineering work required to meet them

• Recruit, onboard, and develop strong engineers; give direct feedback, grow careers, and build a team culture that earns the confidence of enterprise customers

You may be a good fit if you

• Are a skilled engineering manager who takes the craft of management seriously: clear feedback, strong 1:1s, hard conversations handled well, and consistent investment in your team's growth

• Have experience building products for or within financial services—you understand how these organizations work, what they care about, and why trust and reliability aren't negotiable

• Know how to operate in an enterprise sales environment; you're comfortable alongside sales and customer success teams and can hold your own in customer conversations

• Have shipped AI-powered products and developed a grounded, practical understanding of what it takes to make them reliable and useful in high-stakes contexts

• Are experienced with the operational realities of building on third-party platforms—you've thought through degradation strategies, incident response, and the accountability gaps that come with dependencies you don't control

• Thrive in early-growth environments where the product is real but the playbook is still being written

Strong candidates may also have

• Deep domain knowledge in financial services—investment banking, asset management, insurance, corporate finance, or similar—whether from working within these institutions or building products for them

• Direct experience with compliance frameworks relevant to financial services and healthcare, and a track record of owning or driving compliance initiatives within an engineering organization; familiarity with HIPAA is a meaningful differentiator

• Experience managing teams that use AI-assisted coding tools, and a considered perspective on what that means for code review, quality standards, and engineering norms

• Exposure to both product-led growth and direct enterprise sales motions, with an understanding of how engineering decisions interact differently with each

• Vendor management experience—negotiating with, evaluating, or operationalizing third-party technology providers

• Familiarity with model evaluation frameworks and how evals can inform product decisions, not just research ones

Representative projects

• Partnering with an investment banking customer to understand their deal documentation workflow, then working with product to translate that into a concrete engineering roadmap

• Building an incident response and communication playbook for outages or degradation in Microsoft 365 integrations—and running the post-mortems that drive real improvements

• Owning a compliance initiative from scoping through delivery: working with legal and security, defining what engineering needs to build, and getting your team across the line

• Collaborating with a cross-functional team to develop a new AI-powered product feature, and working with stakeholders to ensure its successful launch

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About the Role

As a Technical Program Manager, you will lead initiatives that test the safety and robustness of OpenAI’s models through creative experimentation and structured evaluation. You’ll coordinate efforts across research and engineering teams to transform ambiguous risks into concrete research programs and influence future model development and deployment.

In this role, you will:

• Lead programs that explore unexpected model behaviors and identify failure modes.

• Translate vague or emergent risk signals into clear priorities and actionable research plans.

• Design and run creative evaluations, experiments, and red-teaming campaigns.

• Collaborate with research, product, and deployment teams to integrate findings into model training and deployment cycles.

• Develop repeatable systems for tracking model performance and understanding emerging behavior patterns.

You might thrive in this role if you:

• Have strong experience in technical program management, with excellent organizational and communication skills.

• Are familiar with large language models, prompt engineering, or model evaluation techniques.

• Are comfortable managing fast-paced, high-uncertainty projects and shaping them from the ground up.

• Are creative and resourceful in devising new methods for testing model behavior and performance.

• Can effectively coordinate across technical and non-technical stakeholders to drive alignment and execution.

About the Team

The Human Data team at OpenAI is responsible for identifying and mitigating risks in advanced AI systems by designing evaluations, surfacing vulnerabilities, and collaborating closely with researchers to strengthen model reliability and public trust.

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.

Compensation

• $207K – $285K • 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.

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.

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Microsoft are looking for a talented Machine Learning Software Engineer at their New York office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising artificial intelligence technology. You'll work directly with leadership to shape the company's direction in the technology and simulation markets.

About the Role

As a Member of Technical Staff – Software Engineer & Machine Learning, you will work building AI Insights, a Copilot analytics product that enables our internal stakeholders to move from “What happened?” to “Why did it happen?” in minutes. You’ll design and implement AI-driven trend detection, cohort analysis, and drill-down workflows that connect metrics to real user conversations.

Accountabilities

• Build scalable data pipelines for telemetry ingestion, anomaly detection, and cohort segmentation.
• Implement ML-driven insights (prompted classifiers, anomaly detection) and integrate them into dashboards and APIs.

The Candidate we're looking for

Experience:

• 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.

Technical skills:

• Proven experience leading small engineering and machine learning teams, and collaborating effectively with cross-functional stakeholders including product managers, UX designers, and security specialists.
• Demonstrated interest in Responsible AI.

Personal attributes:

• Hands-on with observability (metrics, tracing, logs) and model evaluation frameworks.

Benefits

• Competitive salary range of USD $139,900 – $274,800 per year.
• Benefits and other compensation.
• Opportunities for professional development and growth within the company.

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Microsoft AI are looking for a talented Senior Product Manager, Copilot AI at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI landscape.

About the Role

As a Senior Product Manager, you will drive Copilot model capabilities such as tool-use to ensure that the language models that power Microsoft Copilot deliver high quality responses to our users whilst being grounded, reliable, and cost-efficient. You will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers.

Accountabilities

• Develop and execute on LLM platform strategy for Copilot that extend language model’s capabilities.
• Prototype approaches by steering language models to drive response quality across a wide range of scenarios.

The Candidate we're looking for

Experience:

• 5+ years experience in product management.
• 3+ years of experience leading ambiguous product areas, defining requirements, developing roadmaps, and working with multi-disciplinary teams to execute them.

Technical skills:

• Hands-on experience with LLM APIs (e.g. OpenAI, Anthropic, Azure OpenAI), embeddings, vector databases, and tool use.
• Hands-on experience with prompt design, context window management, and model evaluation.

Personal attributes:

• Abundance of positive energy, empathy, and kindness.
• Highly effective.

Benefits

• Competitive salary.
• Benefits and other compensation.

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Microsoft are looking for a talented Machine Learning Software Engineer at their Redmond office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As a Member of Technical Staff – Software Engineer & Machine Learning, you will work building AI Insights, a Copilot analytics product that enables our internal stakeholders to move from “What happened?” to “Why did it happen?” in minutes. You’ll design and implement AI-driven trend detection, cohort analysis, and drill-down workflows that connect metrics to real user conversations.

Accountabilities

• Build scalable data pipelines for telemetry ingestion, anomaly detection, and cohort segmentation.
• Implement ML-driven insights (prompted classifiers, anomaly detection) and integrate them into dashboards and APIs.

The Candidate we're looking for

Experience:

• 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.

Technical skills:

• Proven experience leading small engineering and machine learning teams, and collaborating effectively with cross-functional stakeholders including product managers, UX designers, and security specialists.
• Demonstrated interest in Responsible AI.

Personal attributes:

• Hands-on with observability (metrics, tracing, logs) and model evaluation frameworks.

Benefits

• Competitive salary range of USD $139,900 – $274,800 per year.
• Certain roles may be eligible for benefits and other compensation.
• Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

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What you'll do

• Develop and implement computer vision algorithms for tasks such as object detection, recognition, tracking, segmentation, and image classification.
• Design and architect computer vision systems to meet specific requirements and objectives.

What you need

• Bachelor’s or master’s degree in computer science, Electrical Engineering, or a related field.
• Proven experience in developing and implementing computer vision algorithms and models.
• Proficiency in programming languages such as Python or C++.
• Strong understanding of image and video processing techniques and methodologies.
• Familiarity with computer vision libraries such as OpenCV.
• Experience with data collection, annotation, and preparation for model training.
• Ability to evaluate and optimize model performance using appropriate metrics and benchmarks.
• Experience integrating computer vision solutions into software systems or products.
• Strong problem-solving skills and attention to detail.
• Excellent communication and teamwork abilities.

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