Scale's rapidly growing Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world.

Our core work consists of creating custom AI applications that will impact millions of citizens, generating high-quality training data for national LLMs, and upskilling and advisory services to spread the impact of AI.

As a Principal AI Ops Architect, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for our international government partners.

At Scale, we're not just building AI solutions,we're enabling the public sector to transform their operations and better serve citizens through cutting-edge technology.

Responsibilities

• Own the production outcome: Take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies.
• Ensure Full-Stack integrity: Oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment.
• Scale the feedback loop: Build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability.
• Navigate global compliance: Manage the technical lifecycle within diverse regulatory frameworks.
• Incident command: Lead the response for production issues in mission-critical environments, ensuring rapid resolution and building the guardrails to prevent them from happening again.
• Bridge the gap: Translate deep technical performance metrics into clear insights for senior international government officials.
• Drive product evolution: Partner with our Engineering and ML teams to ensure the lessons learned in the field directly influence the technical architecture and decisions of future use cases.

Ideal Candidate

• Experience: 6+ years in a high-impact technical role (SRE, FDE or MLOps) with experience in the public sector.
• Global perspective: Familiarity with international government security standards and the complexities of deploying sovereign AI.
• System architecture proficiency: Proven experience maintaining production-grade applications with a deep understanding of the full request lifecycle-connecting frontend/API layers to the backend and AI core.
• Modern AI Stack expertise: Proficiency in coding and the modern AI infrastructure, including Kubernetes, vector databases, agentic development, and LLM observability tools.
• Ownership: You treat every production deployment as your own. You race toward solving hard problems before the customer even sees them.
• Reliability: You understand that in the public sector, a model failure may be a risk to public safety or privacy.
• Customer communication: The ability to explain to a high-ranking official why the performance of the system has degraded and how we are fixing it.

Benefits

• Competitive salary and benefits package
• Opportunity to work with a leading AI company
• Collaborative and dynamic work environment

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.

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This is a 0→1 build team. We’re looking for a sharp, product-minded Senior Engineer who thrives in ambiguity, moves quickly, and enjoys building new systems from scratch alongside customers and cross-functional partners. You’ll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable, production solutions.

If you like shipping fast, owning outcomes, and working across the stack,from polished frontends to distributed backends to LLM integrations,this role is for you.

Responsibilities

• Own major full-stack product areas, driving features from concept and design through production deployment
• Build intuitive, high-performance frontend experiences using React + TypeScript
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and AI/ML infrastructure
• Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows and decision-making systems
• Ship quickly through tight experimentation loops while maintaining high quality and reliability
• Help define the technical direction and architecture of a brand-new team and product surface
• Adapt across the stack and learn new tools as needed to solve real problems end-to-end

Ideal Experience

• 5+ years of full-time software engineering experience
• 0-1 product build experience
• Familiarity with LLMs, embeddings, vector databases, or modern AI data products/tools
• Experience with distributed systems and cloud-based architectures
• Prior experience mentoring or leading team

What We Value

• Strong product intuition and customer empathy
• Bias toward action and rapid iteration
• Ownership mentality , you see problems through to outcomes
• Comfort collaborating across engineering, product, data science, and applied AI
• Excitement about building agentic systems that make AI genuinely useful in the real world

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Key responsibilities include:

• Delivering experiments at a high velocity and level of quality to engage our customers
• Working across the entire product lifecycle from conceptualization through production
• Being able, and willing, to multi-task and learn new technologies quickly

Ideally, you'd have:

• 7+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)

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.

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.

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You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability.

Key responsibilities include:

• Owning large new areas within our product
• Working across backend, frontend, and interacting with LLMs and ML models
• Delivering experiments at a high velocity and level of quality to engage our customers
• Working across the entire product lifecycle from conceptualization through production

Ideally, you'd have:

• 4+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)

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.

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.

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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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Responsibilities

• Own major full-stack product areas, driving features from design through production deployment.
• Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness.
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components.
• Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences.
• Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers.
• Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP.
• Adapt quickly,learning new technologies, frameworks, and tools as needed across the stack.

Ideal Experience

• 5+ years of full-time engineering experience, post-graduation.
• Strong experience developing full-stack applications using React, TypeScript, and Python.
• Experience scaling or shipping products at high-growth startups.
• Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome).
• Proficiency with SQL and modern API development.
• Experience with Kubernetes, containerization, and microservice architectures.
• Experience working with at least one major cloud provider (AWS, GCP, or Azure).

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.

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You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform.

You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader.

The impact you will have:

• Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimisation, integration with cloud-native services, MLOps, etc.

• Serve as trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems

• Build, scale, and optimise customer AI workloads and apply best-in-class MLOps to productionise these workloads across a variety of domains

• Provide advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, as well as participating in the larger ML SME community in Databricks

• Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks' AI offerings

What we look for:

• 5+ years of hands-on industry ML experience in at least one of the following:

• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.

• AI Engineer: Experience with the latest techniques in LLMs & agentic systems including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI

• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience

• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike

• Passion for collaboration, life-long learning, and driving business value through ML & AI

• [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role

• Can meet expectations for technical training and role-specific outcomes within 3 months of hire

• This role can be remote, but we prefer that you be located in the job listing area and can travel up to 30% when needed

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 utilising 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. For more information regarding which range your location is in visit our page here. Local Pay Range $219,100-$301,300 USD

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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.

You will architect and implement solutions across multiple cloud providers (GCP, Azure, AWS) for customers in diverse, highly-regulated industries like healthcare, telecom, finance, and retail.

Key responsibilities include:

• Architecting multi-cloud systems and abstractions to allow the SGP platform to run on top of existing Cloud providers.
• Using our own data and AI platform to analyse build and test logs and metrics to identify areas for improvement.
• Defining the architectural patterns for our multi-cloud infrastructure to support secure, reliable, and scalable Agentic workflows for enterprise customers.
• Enhancing engineering and infrastructure efficiency, reliability, accuracy, and response times, including CI/CD processes, test frameworks, data quality assurance, end-to-end reconciliation, and anomaly detection.
• Collaborating with platform and product teams to develop and implement innovative infrastructure that scales to meet evolving needs.
• Designing and championing highly scalable, reliable, and low-latency infrastructure and frameworks for building, orchestrating, and evaluating multi-agent systems at enterprise scale.
• Leading the infrastructure roadmap with a strong focus on compliance, privacy, and security standards, including designing change management and data isolation strategies.
• Owning the development and maintenance of our best-in-class Agentic observability platform (logging, metrics, tracing, and analytics) to proactively ensure system health and enable rapid incident response.
• Driving developer efficiency by building automated tooling and championing Infrastructure-as-Code (IaC) paradigms throughout the engineering organization to improve workflows and operational efficiency.

The ideal candidate has proven experience in a senior role, with 5+ years of full-time software engineering experience, and a deep understanding of modern infrastructure practices, including CI/CD, IaC (e.g., Terraform, Helm Charts), container orchestration (e.g., Kubernetes) and observability platforms (e.g., Datadog, Prometheus, Grafana).

Extensive experience with at least one major cloud provider (AWS, Azure, or GCP) and strong knowledge of security and compliance in enterprise environments, with a focus on access management, data isolation, and customer-specific VPC setups is required.

Proficiency in Python or JavaScript/TypeScript, and SQL is also necessary.

Bonus points for hands-on experience and a passion for working with Agents, LLMs, vector databases, and other emerging AI technologies.

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You'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Customer Integration & Deployment

Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements.

Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs).

Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows.

Deploy and configure AI models and agents within customer security and compliance boundaries.

AI Agent Development

Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation.

Architect multi-agent systems that orchestrate between different models, tools, and data sources.

Implement evaluation frameworks to measure agent performance and iterate toward business objectives.

Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement.

Prompt Engineering & Optimization

Create sophisticated prompt engineering strategies optimized for customer-specific domains and data.

Build and maintain prompt libraries, templates, and best practices for customer use cases.

Conduct systematic prompt experimentation and A/B testing to improve model outputs.

Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate.

Leadership & Collaboration

Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts.

Lead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration.

Work closely with Scale's product and engineering teams to translate customer needs into product improvements.

Document technical architectures, integration patterns, and best practices.

Problem Solving & Innovation

Debug complex technical issues across the entire stack, from data pipelines to model outputs.

Rapidly prototype solutions to unblock customers and prove out new use cases.

Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems.

Identify opportunities for productization based on common customer patterns.

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Scale's rapidly growing Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world.

Our core work consists of creating custom AI applications that will impact millions of citizens, generating high-quality training data for national LLMs, and upskilling and advisory services to spread the impact of AI.

As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for our international government partners.

Responsibilities

• Own the production outcome: Take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies.

• Ensure Full-Stack integrity: Oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment.

• Scale the feedback loop: Build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability.

• Navigate global compliance: Manage the technical lifecycle within diverse regulatory frameworks.

• Incident command: Lead the response for production issues in mission-critical environments, ensuring rapid resolution and building the guardrails to prevent them from happening again.

• Bridge the gap: Translate deep technical performance metrics into clear insights for senior international government officials.

• Drive product evolution: Partner with our Engineering and ML teams to ensure the lessons learned in the field directly influence the technical architecture and decisions of future use cases.

Ideal Candidate

• Experience: 6+ years in a high-impact technical role (SRE, FDE or MLOps) with experience in the public sector.

• Global perspective: Familiarity with international government security standards and the complexities of deploying sovereign AI.

• System architecture proficiency: Proven experience maintaining production-grade applications with a deep understanding of the full request lifecycle-connecting frontend/API layers to the backend and AI core.

• Modern AI Stack expertise: Proficiency in coding and the modern AI infrastructure, including Kubernetes, vector databases, agentic development, and LLM observability tools.

• Ownership: You treat every production deployment as your own. You race toward solving hard problems before the customer even sees them.

• Reliability: You understand that in the public sector, a model failure may be a risk to public safety or privacy.

• Customer communication: The ability to explain to a high-ranking official why the performance of the system has degraded and how we are fixing it.

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.

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You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform.

Your responsibilities will include:

• Architecting production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc.

• Serving as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems

• Building, scaling, and optimizing customer AI workloads and applying best-in-class MLOps to productionize these workloads across a variety of domains

• Providing advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, as well as participating in the larger ML SME community in Databricks

• Collaborating cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks' AI offerings

We are looking for someone with 5+ years of hands-on industry ML experience in at least one of the following areas:

• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.

• AI Engineer: Experience with the latest techniques in LLMs & agentic systems including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI

A graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience is also required.

Additionally, experience communicating and/or teaching technical concepts to non-technical and technical audiences alike is highly valued.

The salary range for this position is $180,000-$247,500 USD, depending on location.

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As a Software Engineer on the Databases team, you will architect and operate database systems that both enable millions of users to interact with Claude and support cutting-edge AI research.

This is a unique opportunity to tackle database challenges at unprecedented scale. You'll develop the database strategy for Anthropic, design systems that handle billions of API requests, create storage solutions that work seamlessly across GCP, AWS, and diverse deployment models, and build the reliable data layer that accelerates research experimentation.

Responsibilities:

• Drive the technical direction for database solutions used across Product and Research

• Design and implement database solutions that scale to support millions of users across Claude's product ecosystem

• Build and scale database systems through 100x+ growth while maintaining reliability and performance

• Architect data storage solutions that work seamlessly across GCP, AWS, first-party deployments, third-party deployments, and other environments

• Develop database infrastructure that serves both product and research workloads with different performance characteristics

• Partner with product and research teams to understand data requirements and build infrastructure that accelerates innovation

• Optimize database performance, reliability, and cost efficiency at massive scale

• Make critical build vs. buy decisions for database technologies

You might be a good fit if you:

• Have 10+ years of experience in a Software Engineer role, building and scaling database systems

• Have 3+ years of experience leading large scale, complex projects or teams as an engineer or tech lead

• Possess deep expertise in distributed database architectures and OLTP systems at scale

• Have successfully scaled databases through massive growth at high-growth companies

• Can balance the speed of a startup environment with the reliability needs of production systems

• Excel at technical leadership and cross-functional collaboration

• Are passionate about building the data layer that enables next-generation AI capabilities

Strong candidates may also have:

• Deep expertise scaling PostgreSQL, MySQL, DynamoDB, or similar database systems

• Experience with Redis, Temporal, vector databases, or async job processing frameworks

• Experience building multi-cloud or hybrid cloud database solutions

• Knowledge of database orchestration and automation at scale

• Background at companies known for database excellence

Note: Prior AI/ML infrastructure experience is not required. We value deep infrastructure/databases expertise from any domain.

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This is a principal software engineering role that focuses on enhancing the vector and keyword search functionality within Elasticsearch, covering the design and implementation of new search features, enhancements to existing search functionality, and resolving bugs.

Our company is distributed by intention. We hire the best engineers we can find wherever they are, whoever they are. We collaborate across continents every day over email, GitHub, Zoom, and Slack. At our best, we write fast, scalable and intuitive software. We believe that the best way to do that is to empower individual engineers, code review every change, decide big things by consensus, and strive for incremental improvements.

What You Will Be Doing

• Lead initiatives within Elasticsearch to produce an industry-leading search engine offering, supplying unparalleled speed and relevance in search.
• Contribute to Elasticsearch full time, building new search features and fixing intriguing bugs, all while making the code easier to understand. Sometimes you'll need to invent a new algorithm or data structure. Or find one and implement it. Sometimes you'll need to get close to the operating system and hardware.
• Work with a globally distributed team of experienced engineers focused on the search capabilities of Elasticsearch.
• Be an expert on Elasticsearch search relevance. You'll identify and drive improvements in this area based on your questions and your instincts.
• Work with community members from all over the world on issues and pull requests, sometimes triaging them and handing them off to other experts and sometimes handling them yourself.
• Write idiomatic modern Java -- Elasticsearch is 99.8% Java!

What You Bring

• Professional experience with search and vector databases, and you used HNSW, IVF, or other relevant algorithms and libraries on search platforms at scale.
• You have strong skills in core Java and are conversant in the standard library of data structures and concurrency constructs, as well as other features like lambdas.
• You work with a high level of autonomy, and are able to take on projects and guide them from beginning to end. This covers both technical design and working with other engineers to develop needed components.
• You're comfortable developing collaboratively. Giving and receiving feedback on code and approaches and APIs is hard! Bonus points if you've collaborated over the internet because that's harder. Double bonus points for asynchronous collaboration over the internet. That's even harder, but we do it anyway because it's the best way we know how to build software.
• You've used several data storage technologies like Elasticsearch, Solr, PostgreSQL, MongoDB, or Cassandra and have some idea how they work and why they work that way.
• You have excellent verbal and written communication skills. Like we said, collaborating on the internet is hard. We try to be respectful, empathetic, and trusting in all of our interactions. And we'd expect that from you too.

Bonus Points

• You've built things with Elasticsearch before.
• You've worked with open source projects and are familiar with different styles of source control workflow and continuous integration.
• You have experience designing, leading and owning cross-functional initiatives

Compensation for this role is in the form of base salary. This role does not have a variable compensation component. The typical starting salary range for new hires in this role is listed below.

These ranges represent the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the ranges may be modified in the future.

An employee's position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.

Elastic believes that employees should have the opportunity to share in the value that we create together for our shareholders. Therefore, in addition to cash compensation, this role is currently eligible to participate in Elastic's stock program. Our total rewards package also includes a company-matched Registered Retirement Savings Plan (RRSP) with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being.

The typical starting salary range for this role is: $154,000-$243,600 CAD

Additional Information - We Take Care of Our People

As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.

We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do.

• Competitive pay based on the work you do here and not your previous salary
• Health coverage for you and your family in many locations
• Ability to craft your calendar with flexible locations and schedules for many roles
• Generous number of vacation days each year
• Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service
• Up to 40 hours each year to use toward volunteer projects you love
• Embracing parenthood with minimum of 16 weeks of parental leave

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As a Software Engineer on the Databases team, you will architect and operate database systems that both enable millions of users to interact with Claude and support cutting-edge AI research.

This is a unique opportunity to tackle database challenges at unprecedented scale. You'll develop the database strategy for Anthropic, design systems that handle billions of API requests, create storage solutions that work seamlessly across GCP, AWS, and diverse deployment models, and build the reliable data layer that accelerates research experimentation.

Responsibilities:

• Drive the technical direction for database solutions used across Product and Research
• Design and implement database solutions that scale to support millions of users across Claude's product ecosystem
• Build and scale database systems through 100x+ growth while maintaining reliability and performance
• Architect data storage solutions that work seamlessly across GCP, AWS, first-party deployments, third-party deployments, and other environments
• Develop database infrastructure that serves both product and research workloads with different performance characteristics
• Partner with product and research teams to understand data requirements and build infrastructure that accelerates innovation
• Optimize database performance, reliability, and cost efficiency at massive scale
• Make critical build vs. buy decisions for database technologies

You might be a good fit if you:

• Have 10+ years of experience in a Software Engineer role, building and scaling database systems
• Have 3+ years of experience leading large scale, complex projects or teams as an engineer or tech lead
• Possess deep expertise in distributed database architectures and OLTP systems at scale
• Have successfully scaled databases through massive growth at high-growth companies
• Can balance the speed of a startup environment with the reliability needs of production systems
• Excel at technical leadership and cross-functional collaboration
• Are passionate about building the data layer that enables next-generation AI capabilities

Strong candidates may also have:

• Deep expertise scaling PostgreSQL, MySQL, DynamoDB, or similar database systems
• Experience with Redis, Temporal, vector databases, or async job processing frameworks
• Experience building multi-cloud or hybrid cloud database solutions
• Knowledge of database orchestration and automation at scale
• Background at companies known for database excellence

Note: Prior AI/ML infrastructure experience is not required. We value deep infrastructure/databases expertise from any domain.

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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Key responsibilities include:

• Creating high-quality technical videos focused on search, AI/ML, and Elasticsearch as a platform
• Exploring and explaining how Elasticsearch integrates with other tools and protocols in the ecosystems
• Prototyping and publishing sample applications showing end-to-end Search + AI use cases
• Bringing a creative twist to Elastic's content creation to keep existing users engaged and capture new audiences

Requirements include:

• A proven track record of creating highly technical and engaging videos
• Strong technical background and experience in an engineering role
• Empathy for the community to help them solve their issues, provide internal feedback to make Elastic’s products better, and fix problems where possible
• Comfortable working with a highly distributed team
• Experience working self-driven and self-organizing
• Excellent spoken and written communication skills in English

Additional benefits include competitive pay, health coverage for you and your family, ability to craft your calendar with flexible locations and schedules, generous number of vacation days each year, increase your impact through financial donations and service, and embracing parenthood with minimum of 16 weeks of parental leave.

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As part of a global AI engineering team spanning the US and India, you'll provide technical leadership, write hands-on code, and mentor peers while shaping platform capabilities that empower applied AI use cases across Okta.

Responsibilities:

• Design and develop platform services for AI agents, including orchestration, lifecycle management, observability, and guardrails.
• Collaborate daily with US counterparts to align on architecture, share roadmaps, and ensure global consistency.
• Build scalable APIs, frameworks, and reusable components that enable teams to rapidly create and deploy secure AI agents.
• Contribute to monitoring, evaluation, and governance features that make AI adoption safe, reliable, and enterprise-ready.
• Partner with Applied AI engineers, product managers, and designers to ensure the platform supports real-world use cases.
• Mentor engineers within the India team, raising the bar on technical craftsmanship and platform design.
• Stay current with advances in agentic AI, orchestration frameworks, and enterprise AI infrastructure.

Requirements:

• 8+ years of software engineering experience, with at least 3+ years in AI/ML platforms or intelligent automation.
• 5+ years in modern frontend frameworks (e.g., React, Vue) with the ability to build functional UI components, integrate APIs, and maintain design consistency.
• Knowledge on Python preferred, Java, Node.js, or Go.
• Expertise in AI agent frameworks (LangGraph preferred), orchestration systems, vector databases, RAG pipelines, and monitoring/observability.
• Strong background in distributed systems, APIs, microservices, container orchestration (ECS/EKS), and cloud platforms (AWS/GCP/Azure).
• Experience building secure, enterprise-grade infrastructure for AI or ML workloads.
• Ability to influence architecture decisions, align with a global team, and set technical direction locally.
• Strong communication and collaboration skills to work effectively across time zones and global teams.

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With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.

The Opportunity:

ZoomInfo built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering,the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.

This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You'll transform how ZoomInfo's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment.

The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.

We've transitioned to AI-first product thinking company-wide. The context pipelines exist but remain nascent,creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.

What You'll Do:

Architect Context Acquisition Pipelines

Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.

Own the Context Layer Platform

Build infrastructure serving multiple product teams,Copilot, GTM Studio, MarketingOS,as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity.

Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.

Drive Quality Through Measurement

Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production.

Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.

Navigate Emerging Research

Context engineering evolves weekly. You'll continuously evaluate innovations,GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques,determining which advances warrant production investment versus which remain academic curiosities.

Bring external best practices to ZoomInfo while contributing learnings back to the broader community.

Orchestrate Cross-Functional Execution

Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features.

Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.

What You’ll Bring:

• 4-6 years of product management experience with 2+ years in ML/AI infrastructure

• Direct experience with production RAG systems, vector databases, or semantic search, context management

• Experience with graph databases (e.g. Neo4j)

• Track record building platform products serving multiple internal or external customers

• Familiarity with context compression, embedding models, and retrieval evaluation frameworks

• History of defining product vision in nascent technical domains where best practices are still emerging

Who You Are:

Technical Foundation

Expert-level understanding of RAG system architecture,you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth.

You've built or significantly contributed to production retrieval systems, not just managed them at arm's length.

Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.

Platform Product Mindset

Experience building infrastructure products where internal engineering teams are your customers.

You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.

You understand platform economics,how each additional team using your infrastructure increases its value through shared learnings and amortized costs.

Intellectual Velocity

You read recent research papers from arXiv, ACL, NeurIPS.

You prototype emerging techniques to understand their practical constraints.

You maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates.

The discipline moves too fast for static expertise,continuous learning is non-negotiable.

Strategic Communication

You translate between technical depth and business impact fluently.

You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.

You can communicate to engineers why business constraints require shipping 'good enough' in 3 weeks rather than 'optimal' in 3 months.

You influence without formal authority through data, clear reasoning, and earned credibility.

The Environment:

Reporting & Collaboration

Report to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo's Intelligence team.

Work alongside PMs responsible for signals and ML scoring/recommendation models.

Together, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.

Pace & Problems

Fast-moving engineering team that understands the space.

Company-wide AI adoption push creates both urgency and opportunity.

Expect interesting problems: How do we maintain sub-200ms retrieval latency at scale?

When does GraphRAG justify its indexing cost?

How do we balance context freshness with cache efficiency?

You'll shape answers that become architectural patterns across the organization.

Impact

Define a nascent discipline at a company that's already AI-first in product thinking and organizational structure.

Your architectural decisions compound,every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven't imagined yet.

This is infrastructure work with direct line-of-sight to customer value.

#LI-PS1 #LI-remote

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This is a principal software engineering role that focuses on enhancing the vector and keyword search functionality within Elasticsearch, covering the design and implementation of new search features, enhancements to existing search functionality, and resolving bugs.

As a Principal Software Engineer, you will lead initiatives within Elasticsearch to produce an industry-leading search engine offering, supplying unparalleled speed and relevance in search. You will contribute to Elasticsearch full time, building new search features and fixing intriguing bugs, all while making the code easier to understand. Sometimes you'll need to invent a new algorithm or data structure. Or find one and implement it. Sometimes you'll need to get close to the operating system and hardware.

You will work with a globally distributed team of experienced engineers focused on the search capabilities of Elasticsearch. You will be an expert on Elasticsearch search relevance. You'll identify and drive improvements in this area based on your questions and your instincts.

You will work with community members from all over the world on issues and pull requests, sometimes triaging them and handing them off to other experts and sometimes handling them yourself. You will write idiomatic modern Java -- Elasticsearch is 99.8% Java!

Professional experience with search and vector databases, and you used HNSW, IVF, or other relevant algorithms and libraries on search platforms at scale. Strong skills in core Java and are conversant in the standard library of data structures and concurrency constructs, as well as other features like lambdas. You work with a high level of autonomy, and are able to take on projects and guide them from beginning to end. This covers both technical design and working with other engineers to develop needed components.

You're comfortable developing collaboratively. Giving and receiving feedback on code and approaches and APIs is hard! Bonus points if you've collaborated over the internet because that's harder. Double bonus points for asynchronous collaboration over the internet. That's even harder, but we do it anyway because it's the best way we know how to build software.

You've used several data storage technologies like Elasticsearch, Solr, PostgreSQL, MongoDB, or Cassandra and have some idea how they work and why they work that way. Excellent verbal and written communication skills. Like we said, collaborating on the internet is hard. We try to be respectful, empathetic, and trusting in all of our interactions. And we'd expect that from you too.

Bonus points if you've built things with Elasticsearch before. You've worked with open source projects and are familiar with different styles of source control workflow and continuous integration. You have experience designing, leading and owning cross-functional initiatives.

Compensation for this role is in the form of base salary. This role does not have a variable compensation component. The typical starting salary range for new hires in this role is $159,800-$252,800 USD. In select locations (including Seattle WA, Los Angeles CA, the San Francisco Bay Area CA, and the New York City Metro Area), an alternate range may apply as specified below.

Elastic believes that employees should have the opportunity to share in the value that we create together for our shareholders. Therefore, in addition to cash compensation, this role is currently eligible to participate in Elastic's stock program. Our total rewards package also includes a company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being.

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This is a principal software engineering role that focuses on enhancing the vector and keyword search functionality within Elasticsearch, covering the design and implementation of new search features, enhancements to existing search functionality, and resolving bugs.

Our company is distributed by intention. We hire the best engineers we can find wherever they are, whoever they are. We collaborate across continents every day over email, GitHub, Zoom, and Slack. At our best, we write fast, scalable and intuitive software. We believe that the best way to do that is to empower individual engineers, code review every change, decide big things by consensus, and strive for incremental improvements.

As a Principal Software Engineer, you will lead initiatives within Elasticsearch to produce an industry-leading search engine offering, supplying unparalleled speed and relevance in search. You will contribute to Elasticsearch full time, building new search features and fixing intriguing bugs, all while making the code easier to understand. Sometimes you'll need to invent a new algorithm or data structure. Or find one and implement it. Sometimes you'll need to get close to the operating system and hardware.

You will work with a globally distributed team of experienced engineers focused on the search capabilities of Elasticsearch. You will be an expert on Elasticsearch search relevance. You'll identify and drive improvements in this area based on your questions and your instincts.

You will work with community members from all over the world on issues and pull requests, sometimes triaging them and handing them off to other experts and sometimes handling them yourself. You will write idiomatic modern Java -- Elasticsearch is 99.8% Java!

We're looking for someone with professional experience with search and vector databases, and you used HNSW, IVF, or other relevant algorithms and libraries on search platforms at scale. You have strong skills in core Java and are conversant in the standard library of data structures and concurrency constructs, as well as other features like lambdas.

You work with a high level of autonomy, and are able to take on projects and guide them from beginning to end. This covers both technical design and working with other engineers to develop needed components. You're comfortable developing collaboratively. Giving and receiving feedback on code and approaches and APIs is hard! Bonus points if you've collaborated over the internet because that's harder. Double bonus points for asynchronous collaboration over the internet. That's even harder, but we do it anyway because it's the best way we know how to build software.

You've used several data storage technologies like Elasticsearch, Solr, PostgreSQL, MongoDB, or Cassandra and have some idea how they work and why they work that way. You have excellent verbal and written communication skills. Like we said, collaborating on the internet is hard. We try to be respectful, empathetic, and trusting in all of our interactions. And we'd expect that from you too.

Bonus points if you've built things with Elasticsearch before. You've worked with open source projects and are familiar with different styles of source control workflow and continuous integration. You have experience designing, leading and owning cross-functional initiatives.

At Elastic, we strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. We offer competitive pay based on the work you do here and not your previous salary. We provide health coverage for you and your family in many locations. We allow you to craft your calendar with flexible locations and schedules for many roles. We offer generous vacation days each year. We increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service. We provide up to 40 hours each year to use toward volunteer projects you love. We embracing parenthood with minimum of 16 weeks of parental leave.

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We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid's network covers 12,000 financial institutions across the US, Canada, UK and Europe.

The AI Applications Team You will have the opportunity to join as one of the founding members of this newly formed team that is dedicated to consolidating and rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaid's transformation into an AI-first company.

In this role you will lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include:

• Develop and evolve the preferred integration pattern for Plaid with AI providers - from API adaptations to building the official Plaid MCP Servers, and beyond

• Redefine how Plaid's consumer link experience embed into conversational interfaces in the most seamless way

• Architect the trust layer for the future of agentic commerce that will become the industry standard

Additionally you will be expected to scale and extend our existing successful bets on AI-powered customer experience; examples include:

• Make the next step-function improvement in our homegrown customer support agent

• Land our multi-turn and multi-agent system that powers a truly delightful experience for our customers; define how to scalably run offline evaluation for complex multi-turn open-ended tasks; research and prototype how Human-In-The-Loop - Reinforcement Learning (RLHF) can power an insights flywheel; pioneer the architecture for customer-specific long-term memory, etc.

• Extend our agentic system to support other critical parts of the customer journey, starting with areas with the highest ROI - top-of-funnel product recommendation, customer onboarding and risk diligence, customer activation and assistance for faster productionization, as well as upselling and cross-selling of Plaid products

You will have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI < Fintech will be heading longer term.

Responsibilities

• Build across the stack. Design, develop, and maintain scalable backend services and APIs, as well as intuitive, high-quality frontend applications that bring those systems to life.

• Work with other AI engineers, software engineers and machine learning engineers to architect, design and implement GenAI-powered products and features

• Collaborate across functions to understand user needs, propose and implement AI-powered solutions where they’re expected to have the highest impact

• Design and execute rapid experiments to push the boundaries on potential business impact from emerging AI capabilities, with a focus on minimal viable testing approaches

• Balance creative exploration of possibilities with rigorous evaluation of technical feasibility, product potential and business impact

Requirements

• Experience building backend services and working with microservices or service-oriented architectures

• Strong working knowledge of HTML, CSS, JavaScript, and modern frontend frameworks or libraries, with comfort building user-facing experiences

• Hands-on experience working with LLMs to build products and shipping them to product with iterating with real user feedback - including but not limited to:

• Prompt engineering

• Fine-tuning

• Retrieval augmented generation (RAG)

• Semantic search

• Vector database and embedding models

• Agent orchestration framework

• Evaluation and monitoring framework of open-ended tasks

• Streaming and SSE

• Common UX and design patterns for GenAI-powered products

• Strong debugging and monitoring experience for production systems

• Ability to deeply understand customer and user needs through user research and rapid experimentation - be your own technical PM

• Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility), which is critical for driving 0 ->1 projects

• Extremely curious and passionate about working in GenAI applications space

Nice-to-Haves

• Experience training and/or serving ML models in production, or fine-tuning LLMs for domain-specific use cases

• Comfortable operating in privacy/PII-sensitive environments and applying compliance mitigations

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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We currently have 614 open roles

Principal AI Engineer – Enterprise Agentic AI

Job Summary

You are a highly skilled, hands-on engineer with a passion for embedding advanced AI into the heart of enterprise business systems. With a robust enterprise background and a minimum of 10 years in software engineering, you thrive on architecting and delivering production-grade AI solutions that drive real business outcomes.

Key Responsibilities

• Lead the architecture and hands-on development of enterprise agentic AI platforms across core business domains.
• Build and deploy AI copilots and multi-agent workflows for business teams (Legal, Finance, Sales, HR, Operations) using Microsoft AI Copilot, SAP BTP, Salesforce, and other platforms.
• Embed AI directly into enterprise workflows, ensuring seamless integration with ERP and CRM systems.
• Design, implement, and govern secure AI solutions utilizing MCP (Model Context Protocol) and A2A (Agent-to-Agent) orchestration standards.
• Develop scalable APIs and microservices in Python and C#, supporting robust, production-ready AI deployments.
• Ensure production readiness through best practices in CI/CD, observability, reliability, and continuous evaluation.
• Mentor engineers, lead architectural discussions, and collaborate with cross-functional business and technical stakeholders.

Impact

• Drive the adoption of agentic AI across Synopsys' core business systems, setting standards for enterprise AI integration and governance.
• Deliver high-visibility, high-impact AI solutions that transform business processes and decision-making for teams company-wide.
• Shape the architecture and best practices for scalable, secure, and reliable AI across hybrid and multi-vendor environments.
• Empower business teams to leverage AI copilots and intelligent workflows for increased productivity and innovation.
• Mentor and upskill engineering teams, fostering a culture of excellence, collaboration, and forward-thinking AI adoption.
• Ensure AI solutions meet enterprise standards for security, compliance, reliability, and operational excellence.

Requirements

• 10–15 years of software engineering experience, with a focus on enterprise systems.
• Expert-level proficiency in both Python and C#.
• Hands-on experience building and operating production AI/GenAI systems integrated with ERP and CRM platforms.
• Deep understanding of agentic AI, RAG, multi-step workflows, and enterprise-grade AI architecture.
• Proven experience with Microsoft AI Copilot, Copilot Studio, Microsoft AI Foundry, SAP BTP, and Salesforce AI (including Einstein and agentic capabilities).
• Expertise in microservices, API development, CI/CD pipelines, and cloud deployments.
• Experience with SQL, NoSQL, and vector databases.
• Preferred: Familiarity with LangChain, LangGraph, LlamaIndex, MCP, A2A, and prior support for business functions such as Legal, Finance, Sales, or HR.

Who You Are

• Enterprise-minded engineer with a strong platform and architecture focus.
• Hands-on leader who enjoys mentoring and collaborating across business and technical teams.
• Pragmatic, delivery-oriented, and able to translate business needs into robust AI solutions.
• Adaptable and comfortable working in fast-changing, multi-vendor, and hybrid environments.
• Excellent communicator, able to articulate complex technical concepts to diverse audiences.
• Committed to continuous learning and keeping pace with the latest in AI and enterprise technology.

The Team You’ll Be A Part Of

You will join a cross-functional, forward-thinking engineering team responsible for embedding trustworthy, scalable AI into Synopsys’ most critical business systems. Our team partners closely with business stakeholders and platform owners to deliver agentic AI workflows that create measurable business value. We are passionate about innovation, operational excellence, and setting the standard for enterprise AI adoption.

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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Founded by British makeup artist and beauty entrepreneur Charlotte Tilbury MBE in 2013, Charlotte Tilbury Beauty has revolutionised the face of the global beauty industry.

The AI & ML Engineering team accelerates the adoption of AI across the business, championing innovation while ensuring our machine learning products are robust, scalable, and cost-efficient.

Responsibilities:

Partner with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions.

Design and implement agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack.

Build and maintain production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing optimisation modules, leveraging supervised, unsupervised and/or econometric modelling approaches).

Develop APIs and microservices for AI/ML solutions, ensuring security, scalability, and observability.

Implement CI/CD for ML services, writing infrastructure as code, and monitoring for model/data drift and performance.

Establish robust guardrails for safe AI usage, including prompt security, practical evaluation frameworks, and compliance with privacy regulations.

Drive and evangelize best practices, reusable templates, and documentation to scale AI/ML delivery across the business.

Collaborate with data engineers, data scientists, front & back-end engineers, product managers, legal & infosec colleagues to deliver impactful solutions end-to-end.

Who you will work with

The AI & ML Engineer Lead and the wider data team.

About you

The role requires a blend of technical depth and product sense, including:

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

Strong Python engineering skills (FastAPI, testing, typing) and experience with cloud-native development (GCP preferred).

Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph.

Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring.

Proven experience deploying and operating ML systems in production (batch and real-time).

Familiarity with RAG architectures, prompt engineering, and evaluation techniques.

Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling).

Excellent communication skills and ability to work with non-technical stakeholders.

In addition to the above, we would LOVE if you have:

Experience with vector databases and retrieval strategies.

Knowledge of recommender systems and ranking models.

Familiarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize).

Exposure to feature stores, data lineage, and observability stacks.

Experience in e-commerce or retail environments.

Demonstrable ability to weigh up build/build/configure decisions in the LLM space.

Why join us?

Be a part of this values driven, high growth, magical journey with an ultimate vision to empower everyone, everywhere to be the best version of themselves.

We’re a hybrid model with flexibility, allowing you to work how best suits you.

25 days holiday (plus bank holidays) with an additional day to celebrate your birthday.

Inclusive parental leave policy that supports all parents and carers throughout their parenting and caring journey.

Financial security and planning with our pension and life assurance for all.

Wellness and social benefits including Medicash, Employee Assist Programs and regular social connects with colleagues.

Bring your furry friend to work with you on our allocated dog friendly days and spaces.

And not to forget our generous product discount and gifting!

At Charlotte Tilbury Beauty, our mission is to empower everybody in the world to be the most beautiful version of themselves.

We celebrate and support this by encouraging and hiring people with diverse backgrounds, cultures, voices, beliefs, and perspectives into our growing global workforce.

By doing so, we better serve our communities, customers, employees - and the candidates that take part in our recruitment process.

If you want to learn more about life at Charlotte Tilbury Beauty please follow our LinkedIn page!

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As a senior developer at BlackRock, you'll work for a leading financial company and be part of a team creating next-generation technology. You will be working as a key member of our core Private Markets technology team, based in London, and will be responsible for delivering high efficiency, high availability, concurrent and fault tolerant software systems using API-First principles.

Your Responsibilities

• Work closely alongside the Engineering Lead to deliver on the technical strategy for your team.
• Collaborate directly with colleagues across Private Markets and the wider Aladdin organization to build and deliver market leading software at pace.
• Deliver high efficiency, high availability, concurrent and fault tolerant software systems using API-First principles.
• Collaborate with product managers, designers, and other stakeholders to understand requirements and translate them into technical specifications.
• Design, develop, and implement secure, scalable, and reliable micro-services to deliver client-facing applications using a Java backend and React frontend.
• Optimize performance and reliability through continuous testing, debugging, and refinement.
• Stay abreast of industry trends and emerging technologies to drive innovation and maintain a competitive edge, with a particular focus on AI.
• Provide technical leadership and mentorship to more junior developers, fostering a culture of learning and growth within the team.

Requirements

• A Bachelor’s degree in Computer Science, Engineering, or a related field.
• 7+ years of professional experience in Java development, with a focus on building client-facing APIs or platforms
• Strong written and verbal communication skills, with experience managing and mentoring diverse teams.
• Knowledge of building applications using AI tooling and frameworks (e.g. LLMs, prompt engineering, vector databases).
• Experience integrating AI/ML services into backend systems and APIs.
• Familiarity with AI-driven development practices and tools for code generation and testing.
• Strong proficiency in Java and related frameworks (e.g. Spring Boot, Hibernate).
• Some experience in React or similar, and modern JavaScript/TypeScript.
• Solid experience with object-oriented design and applying standard design patterns
• Experience designing, building, and deploying scalable microservices using API-first principles with REST and/or gRPC.
• A strong appreciation for best practices, attention to detail, and building secure and stable systems.
• A collaborative mindset, with a drive to build high-quality software efficiently.
• Experience with API authentication and authorisation mechanisms (e.g. OAuth, JWT).
• Hand-on experience with engineering tools such as Git, Maven, unit and integration testing frameworks, and mocking libraries.
• Excellent problemsolving skills and attention to detail.
• Exposure to Docker, Kubernetes, and cloud services in an enterprise environment is beneficial.
• Experience with message brokers such as Kafka a plus.
• A friendly, positive, and cando attitude.

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.

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Constructor is seeking a Backend Engineer to join its AI Shopping Agents team. The primary focus of this job is to design, deliver & maintain web and data pipeline services in close collaboration with other engineers.

Responsibilities

• Build, deploy, and support backend services
• Define cloud infrastructure using AWS CloudFormation and maintain CI/CD pipelines with GitHub Actions
• Improve and operate our observability stack
• Collaborate with technical and non-technical stakeholders to design, develop, and refine features
• Communicate effectively with stakeholders within and outside the team
• Contribute to data processing pipelines and ETL processes

Requirements

• Strong proficiency in Python and server-side web development (API design, concurrency/asynchronous programming)
• Experience designing, building, and operating production backend services (performance, reliability, on-call/operations mindset)
• Experience with Infrastructure as Code and cloud resource management (AWS preferred; Azure/GCP also fine)
• Hands-on experience building or maintaining CI/CD pipelines
• Experience with observability: metrics/logs/traces, dashboards, and alerting
• Experience working with databases, including at least one relational and one NoSQL system (e.g., PostgreSQL, DynamoDB)

Nice to Haves

• Experience with high-load and/or real-time systems
• Experience with distributed/service-oriented architectures, including interface definition and binary RPC (e.g., Protobuf/gRPC)
• Familiarity with additional vector databases
• Experience contributing to or owning ETL/data pipeline systems at scale

Benefits

• Work with smart and empathetic people who will help you grow and make a meaningful impact.
• Regular team offsite events to connect and collaborate.
• Fully remote team - choose where you live.
• Unlimited vacation time - we strongly encourage all of our employees take at least 3 weeks per year.
• Work from home stipend! We want you to have the resources you need to set up your home office.
• Apple laptops provided for new employees.
• Training and development budget for every employee, refreshed each year.
• Maternity & Paternity leave for qualified employees.
• Base salary: $80k–$120K USD, depending on knowledge, skills, experience, and interview results.
• Stock options - offered in addition to the base salary

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About the team: The team includes Backend Engineers, Full-stack Developers, ML Engineers, Data Scientists, and Frontend Developers—collaborating to deliver intelligent and scalable AI experiences.

Responsibilities:

• Lead and mentor a team of backend and frontend developers, fostering a high-performing, collaborative engineering culture.
• Own the technical direction, architecture, and strategy for LLM, AI Agent, and ML initiatives.
• Architect and build AI Agents, RAG pipelines (including multimodal RAG), LLM-based services, and evaluation frameworks.
• Design, build, and deploy scalable AI/ML systems from concept to production.
• Collaborate closely with PMs, ML Engineers, and Data Scientists to convert business requirements into AI-driven solutions.
• Oversee full SDLC for your squad—ensuring strong engineering practices around coding, testing, quality, and reliability.
• Conduct detailed technical reviews and provide guidance to maintain code quality.
• Troubleshoot performance, scalability, and reliability issues in production AI systems.
• Champion continuous improvement, automation, and innovation within the team.
• Keep the team up-to-date with advances in LLMs, LLMOps, fine-tuning techniques, vector search, and generative AI.

Requirements:

• 8–10 years of experience in software development.
• More than 5 years of hands-on experience in AI/ML.
• More than 2 years in an engineering leadership role, preferably leading a cross-functional team.
• Hands-on experience building:
  • AI Agents / Agentic workflows
  • LLM pipelines (inference, orchestration, guardrails)
  • RAG systems (vector search, knowledge bases, multimodal RAG)
  • LLM fine-tuning (SFT, LoRA, instruction tuning)
  • LLMOps workflows (monitoring, evaluation, optimization, versioning)
• Strong understanding of ML algorithms, embeddings, transformers, vector semantics, and distributed systems.
• Experience with Vector Databases such as Elasticsearch, Weaviate, Pinecone, or Milvus.
• Proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling.

Good to have:

• Solid foundation in backend technologies such as Python, Java, Go, Node.js, and experience with Kafka.
• Knowledge of the Gaming industry or Customer Support domain.
• Familiarity with frontend frameworks (React, Angular, Vue.js).
• Exposure to functional programming (Clojure preferred; our team uses Clojure).
• Experience in startup/high-growth environments.
• Open-source contributions in AI/ML/LLM projects.
• Experience with containerization and orchestration (Docker, Kubernetes).

Bonus Points:

• Experience building production-grade LLM-powered automation for customer support or gaming.
• Publications, presentations, or contributions related to LLMs, RAG, or AI Agents.

Benefits:

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.

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Responsibilities:

• Design, develop, and deploy end-to-end GenAI solutions (RAG, AI agents, agentic workflows, prompt engineering).
• Integrate AI solutions into existing systems, applications, and business processes.
• Implement LLMOps practices, including Docker containerization, CI/CD pipelines, and versioning strategies.
• Ensure monitoring, observability, cost optimization, and rollback mechanisms for production AI services.
• Define and execute evaluation frameworks (hallucination metrics, A/B testing, offline/online validation).
• Apply security, compliance, and governance guidelines for GenAI implementations.
• Collaborate with stakeholders and contribute to AI delivery standards and onboarding practices.

Requirements:

• Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
• Very strong expertise in Python and software engineering (APIs, testing, code reviews).
• Practical experience with RAG architectures, vector databases, and agentic AI workflows.
• Hands-on experience deploying production-grade AI services.
• Solid knowledge of Docker and CI/CD pipelines.
• Understanding of ML fundamentals, evaluation concepts, and LLM behavior.
• Familiarity with cloud environments (preferably Azure) and distributed systems.
• Strong analytical and problem-solving skills.
• Very good level of English.
• Autonomous, reliable, and team-oriented mindset.

What you will get:

• A role with true technical ownership: architecture, scaling, and governance decisions that directly impact production AI solutions.
• Complex projects that go beyond “just pipelines” – covering big data processing and large-scale ML/DL deployment.
• Opportunities to deepen your expertise in Databricks, cloud-native ML, and MLOps.
• A team where your input and technical decisions truly matter.
• A competitive package and benefits.

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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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Research Engineer, Retrieval & Search, Applied Engineering

Location

San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $293K – $585K • 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

We bring OpenAI's technology to the world through products like ChatGPT and the OpenAI API.

We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth.

About the Role

We are looking for an experienced Research Engineer to work on retrieval & search problems across our API and ChatGPT. As the AI landscape has evolved over the last few years, retrieval & search have emerged as key use cases for our models, and we are investing in ensuring that we can offer these search-based product experiences for our users. You will be at the center of our retrieval & search efforts as a company, and the progress you drive here will reach millions of end users.

In this role, you will:

• Work on retrieval & search algorithms and methodologies in close collaboration with our research team, including problems in such domains as document search, enterprise search, knowledge retrieval, and web-scale search.

• Deploy these search methodologies into production in both the API and ChatGPT to be used by millions of end users.

• Explore novel research topics in retrieval & search that may inform our product strategy in the medium and long term.

• Partner with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world

You might thrive in this role if you:

• Have extensive prior experience building and maintaining production machine learning systems.

• Have prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases

• Have prior experience building and iterating on internet-scale search systems

• Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done

• Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines

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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San Mateo, CA, United States

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

You Will

As a Senior Machine Learning Engineer on the AI Platform team, you will be a key contributor to building the cutting-edge systems that power AI at Roblox. You will focus on one of three high-impact tracks:

Track 1: AI Platform Projects

• Pioneer next-generation AI tooling to enhance the efficiency, cost, and usability of ML@Roblox.
• Build and maintain core platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.
• Design great developer experiences (paved-road templates, tooling, visualizations) to reduce time-to-production and ensure foundational AI systems are scalable and reliable.

Track 2: Distributed Inference & Systems Optimization

• Architect and implement scalable distributed inference systems for efficiently serving LLMs and Large Recommender Models at massive scale.
• Conduct deep, low-level performance analysis and optimize ML models (using techniques like continuous batching, speculative decoding, and quantization) and systems on GPU architectures to maintain peak performance and stability.

Track 3: Information Retrieval & RAG for Gen AI

• Lead the design and development of Retrieval-Augmented Generation (RAG) systems.
• Build and maintain core information retrieval infrastructure—vector databases and knowledge graphs—to enable accurate grounding of Gen AI models.
• Ship language models and 3D objects as a service for the Roblox community, making creation easier.

You Have

• Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox’s research areas.
• Experience with high performance distributed systems, ML Infrastructure, LLM fine tuning/RL, Information Retrieval and Gen AI context generation.
• Expertise in one or more of the following key areas:
• AI/ML Platform Data stores - Features stores, Vector DBs and Knowledge Graphs.
• LLMs - Fine tuning, Safety.
• Agentic systems - Agent evaluation, context engineering.

• Experience building agentic applications with context for real world applications.
• Collaborative mindset and experience integrating and deploying optimized models with cross-functional teams, including data scientists and software engineers.
• Experience with graph databases and large-scale GNNs (Graph Neural Networks)
• Experience working with Kubernetes
• Experience working with one or more cloud providers (e.g., AWS, Azure, GCP)
• Experience working with high availability systems
• Experience working with ML models, LLMs or other AI systems

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

As you apply, you can find more information about our process by signing up for Speak\_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Annual Salary Range

$195,780—$242,100 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).

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What you'll do

Implement AI solutions to enable efficiency, expansion, and transformation across EA Experiences, directly aligned with EA Experiences' goals. You will:

• Stay informed on the evolution of AI technologies and opportunities to drive efficiency, expansion, and transformation
• Have a deep understanding of AI technologies and solutions, specifically focusing on how they can be integrated into business processes and systems to enhance efficiency
• Collaborate EA Experiences teams to understand their business, processes, opportunities, and challenges.
• Identify opportunities for developing creative AI solutions that enable our teams to focus on creative and innovative work that keeps our fans at the center of what we do
• Be an AI evangelist, promoting the use of AI as a force-multiplier

What you need

• 4+ years experience rapidly prototyping and deploying robust production solutions incorporating software engineering, infrastructure, architectural, and security best practices
• Excellent Python development skills are required, C#, JavaScript, HTML, and CSS are strongly preferred
• Experience using agentic AI software engineering solutions such as Cursor, GitHub CoPilot, or Claude Code
• Extensive experience engineering AI solutions and excellent working knowledge of:
• Prompt and context engineering
• AI agents, agentic architectures, tool calling, and Model Context Protocol (MCP)
• AI assistants, RAG, embeddings, and vector databases
• Model tuning, evaluation, benchmarking, and guardrails
• An excellent understanding of architecture, design principles, and cloud services, such as:
• AWS (Azure, and GCP cloud services are optional)
• Enterprise architecture and microservices design
• Observability and telemetry
• Infrastructure as code and CI/CD
• Data science experience is beneficial
• Content production (e.g. marketing) is strongly beneficial
• Experience navigating the legal, ethical, and security implications for AI
• Thrive working both collaboratively and independently
• Excellent creative, critical thinking, and problem solving skills
• Enjoy and prioritize continual learning- and staying up to date with the newest technologies and solutions and can understand how they can benefit EA Experiences
• Demonstrated ability to clearly and succinctly articulate complex concepts at a senior level
• Experience integrating AI solutions specifically with AAA Console/PC games and businesses is beneficial
• Experience with game engines is beneficial (Unity, Unreal)

Why this matters

This role is a key part of our EA Experiences team, and will help us to create innovative AI solutions that keep our fans at the center of everything we do. As an AI Solutions Engineer, you will be working closely with teams across EA Experiences to develop and implement cutting-edge AI solutions that provide increased efficiency and help our teams focus on what matters most, our fans.

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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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What you'll do

• Develop AI-based foundations and services that allow the Frostbite community to benefit from agentic developer workflows.
• Provide secure and scalable solutions for all stages of the software development lifecycle, including spec-driven coding, validation and releasing.
• Pilot new AI capabilities to assess their impact on developer efficiency and quality.

What you need

• 4+ years of job experience in a hands-on coding role using languages like Python, C# or C++.
• 1+ years of experience using generative AI software engineering solutions such as GitHub CoPilot.
• Proficiency in good development practices (version control, code reviews) and validation (static code analysis, unit testing, CI/CD pipelines).

Why this matters

This role is a key part of Electronic Arts' efforts to transform software engineering and quality processes with the power of generative AI, agents and context engineering. By contributing to this work, you will have a direct impact on the quality and efficiency of game development at Electronic Arts.

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What you'll do

Implement AI solutions to enable efficiency, expansion, and transformation across EA Experiences, directly aligned with EA Experiences' goals. You will:

• Stay informed on the evolution of AI technologies and opportunities to drive efficiency, expansion, and transformation
• Have a deep understanding of AI technologies and solutions, specifically focusing on how they can be integrated into business processes and systems to enhance efficiency
• Lead collaborative efforts with EA Experiences teams to understand their business, processes, opportunities, and challenges.
• Identify opportunities for developing creative AI solutions that enable our teams to focus on creative and innovative work that keeps our fans at the center of what we do
• Be an AI evangelist, promoting the use of AI as a force-multiplier

What you need

• 4 years + experience rapidly prototyping and deploying robust production solutions incorporating software engineering, infrastructure, architectural, and security best practices
• Python development skills, C#, JavaScript, HTML, and CSS is beneficial
• Experience using agentic AI software engineering solutions such as Cursor
• Extensive experience engineering AI solutions and excellent working knowledge of:
• Prompt and context engineering
• AI agents, agentic architectures, tool calling, and Model Context Protocol (MCP)
• AI assistants, RAG, embeddings, and vector databases
• Model tuning, evaluation, benchmarking, and guardrails
• A comprehensive understanding of architecture and design principles, and cloud services, such as:
• AWS, Azure, and GCP cloud services
• Enterprise architecture and microservices design
• Observability and telemetry
• Infrastructure as code and CI/CD
• Experience navigating the legal, ethical, and security implications for AI
• Thrive working both collaboratively and independently
• Excellent creative, critical thinking, and problem solving skills
• Enjoy and prioritize continual learning- and staying up to date with the newest technologies and solutions and can understand how they can benefit EA Experiences
• Demonstrated ability to clearly and succinctly articulate complex concepts at a senior level
• Experience integrating AI solutions specifically with AAA Console/PC games and businesses is beneficial

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