You'll partner with engineering, product, and design teams to build, evaluate, and iterate on scalable solutions that help Pinners find relevant, inspiring content.

In this role, you'll grow your technical expertise while contributing to reliable, high-impact systems.

Responsibilities:

• Conduct applied machine learning research to address product and platform challenges at Pinterest.
• Collect, analyze, and synthesize data to develop data-driven machine learning models.
• Write maintainable, efficient code and contribute to testing and code-review practices.
• Apply machine learning techniques, including natural language processing and graph-based approaches, to modeling and ranking challenges across discovery, ads, and search.
• Design, build, and test recommendation and engagement-prediction models that improve push, email, and in-app notification experiences.
• Develop and iterate on large-scale applied machine learning systems, using evaluation results to improve model performance and reliability.
• Scope, implement, and deliver solutions for moderately complex ML problems, escalating risks and tradeoffs as needed.

Requirements:

• Master's in Computer Science, ML, NLP, Statistics, Information Sciences, or related field required.
• Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval, etc.).
• Experience with big data technologies (e.g., Hadoop/Spark) and scalable real-time systems that process stream data.
• Mastery of at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow).
• Cross-functional collaborator and strong communicator.
• Comfortable solving ambiguous problems and adapting to a dynamic environment.
• Experience in research and in solving analytical problems.

Benefits:

• The minimum and maximum salary for this position is $133,600-153,600.
• Relocation assistance may be eligible.
• In-office requirement: 2-3 weekdays per week in the Zurich office.

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Staff Machine Learning Engineer, CX Intelligence

CEE Intelligence (CEEI) is the ML and intelligence layer for Coinbase's customer engagement ecosystem, building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that power Help Center, chat, voice, agent tooling, and other customer support runtimes. As a Staff Machine Learning Engineer, you'll lead the design and delivery of production ML systems across the org, shaping technical strategy, building scalable intelligence capabilities, and partnering with product and engineering stakeholders to improve resolution quality, automation coverage, safety, and customer experience.

Responsibilities

• Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems.
• Partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes.
• Lead high-impact ML projects end-to-end, from architecture through production, managing priorities, risks, and dependencies.
• Build scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code.
• Mentor engineers and establish team-wide standards for design, testing, observability, and operational excellence.
• Drive design reviews covering security, responsible AI, model quality, and architectural integrity, improving reliability, scalability, and latency across the stack.

Required Skills and Experience

• 8+ years of experience in machine learning and software engineering, with proven success building, scaling, and maintaining production ML systems, data platforms, or analytical services.
• Hands-on expertise with one or more of LLMs, deep learning, graph neural networks, gradient-boosted trees, or regression, combined with strong knowledge of NLP, information retrieval, data mining, or advanced statistics.
• Strong Python skills with demonstrated ability to write maintainable, highly tested production code for ML services.
• Experience mentoring engineers and raising technical standards across a team or organization, including leading design reviews and influencing technical and non-technical stakeholders.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Pay Information

• Annual base salary range (excluding equity and bonus): $218,025-$256,500 USD

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Responsibilities:

• Train, test, and evaluate AI-powered assistants, chatbots, and agentic AI solutions to ensure accurate and reliable employee experiences.
• Design and optimize prompts, conversational flows, intents, responses, and training scenarios across enterprise support platforms.
• Evaluate AI outputs for accuracy, relevance, completeness, tone, and consistency while identifying and mitigating hallucinations, response gaps, or misinformation.
• Monitor and analyze AI performance metrics such as containment, resolution, user satisfaction, engagement, and escalation rates; identify trends, emerging intents, knowledge gaps, and opportunities for automation from conversational data.
• Apply ServiceNow knowledge to translate complex business processes into intuitive conversational scenarios, training data, and evaluation criteria; collaborate with ServiceNow administrators, developers, analysts, and business stakeholders to identify automation opportunities.
• Create and maintain AI training materials, test cases, evaluation criteria, and sample conversations; partner with Knowledge Management teams to improve content quality, discoverability, and AI readiness; keep materials current as products, processes, and policies evolve.
• Support testing and rollout of new AI capabilities and enhancements; provide insights and recommendations based on AI performance, user feedback, and analytics to drive continuous improvement across the People Organization.
• Stay current with developments in generative AI, prompt engineering, conversational AI, agentic AI, and responsible AI; contribute ideas that improve employee self-service and digital support experiences.

Requirements:

• Experience in AI training, prompt engineering, conversational design, NLP, or AI optimization within employee or customer support environments.
• Proven experience training, evaluating, and optimizing AI models, conversational agents, or LLM-powered assistants, including agentic AI solutions.
• Strong understanding of prompt engineering, AI evaluation, model optimization, and responsible AI practices.
• Demonstrated ability to analyze conversational data and performance metrics to drive measurable improvements.
• Working knowledge of ServiceNow or a similar enterprise service-management platform.
• Strong analytical skills and experience with tools such as SQL, Excel, Power BI, Tableau, or ServiceNow Analytics.
• Experience with enterprise conversational AI platforms, knowledge management systems, or AI-enabled search technologies.
• Excellent written and verbal communication skills with the ability to translate technical and business requirements into clear, user-focused content and influence cross-functional stakeholders.
• Understanding of knowledge management, digital employee support, and omnichannel service experiences.

Benefits:

• Annual base salary range: $108,000-$149,000 USD (varies by location)
• Equity (where applicable)
• Bonus
• Health, dental, and vision insurance
• 401(k)
• Flexible spending account
• Paid leave (including PTO and parental leave)

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As an Applied Scientist - PhD Intern, you’ll be part of a community of curious scientists and engineers building foundational and applied ML methods at a scale of millions of daily active users and billions of daily AI requests.

During your 12 or 16-week internship in the Winter, Summer, or Fall, you’ll own a high-impact project from ideation and experimentation to production and publication.

Responsibilities

• Prototype, experiment with, and deploy advanced AI, machine learning, and 3D technologies to solve real-world challenges at scale.
• Translate cutting-edge research into production-ready features alongside cross-functional engineering and product teams.
• Develop, deliver, and optimize solutions that measurably enhance user experience, safety, and creativity on the platform.

Requirements

• Pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.
• Deep expertise in a relevant technical domain, including deep learning, computer vision, NLP, reinforcement learning, or distributed systems.
• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) with experience building and optimizing large-scale systems.
• A strong research track record, evidenced by multiple publications and presentations in highly regarded peer-reviewed venues.

Benefits

• Hourly pay range: $72,$72 USD

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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. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.

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.

At Roblox, we believe advanced research belongs in production, driving core algorithmic advances at a global scale. In this role, you’ll own high-impact projects end-to-end, from initial ideation to production deployment and academic publication, while solving complex, high-dimensional challenges at world-scale concurrency.

Your expertise, across research areas including Computer Vision and Graphics, multimodality, NLP, or recommendation systems, will directly shape the experience for millions of daily active users. Come build the AI-driven infrastructure powering the future of immersive entertainment.

Responsibilities

• Prototype and implement advanced AI and machine learning solutions that drive core platform features and innovation at scale.
• Develop and deploy cutting-edge models (e.g., deep learning, generative AI, multimodal architectures) to solve complex, high-impact technical challenges.
• Collaborate with applied researchers, engineers, and product teams to translate advanced research concepts into production-ready systems.
• Translate complex research into scalable, robust infrastructure that directly impacts hundreds of millions of daily active users.
• Work backward from user and product needs to architecture, and deliver ML solutions that improve platform engagement, safety, and content ecosystem growth.

Requirements

• Possess or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.
• Deep expertise and curiosity in a relevant technical domain, such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.
• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.
• A strong research track record, evidenced by multiple publications and presentations in rigorous peer-reviewed venues.

Benefits

• Annual Salary Range: $196,750,$243,290 USD
• Equity compensation
• Benefits as described on Roblox's benefits page

Work Schedule

• Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday.

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You'll lead a senior-heavy group of 10–12 machine learning engineers, including Staff engineers, who team up dynamically to solve high-priority business problems while staying deep in content understanding (audio, video and text).

Responsibilities

• Lead, coach and grow a group of 10+ senior and Staff machine learning engineers who team up dynamically around business problems while staying deep in their domain.
• Set technical and team strategy for multiple content understanding problems and turn it into clear, achievable plans.
• Own delivery of high-priority company initiatives, from shaping the problem through to measurable impact for listeners.
• Stay close to the technology as a player-coach. Lead technical strategy, reviewing designs, guiding modelling and retrieval choices, and contributing hands-on where it helps the team most.
• Define and champion a practical point of view on how ML engineers use AI tools in their day-to-day work, and help the team realise real productivity gains.
• Partner with product, design, research and other engineering leaders across Spotify to align priorities and share context.

Requirements

• You have deep machine learning expertise and Staff-level technical judgement, and you're comfortable getting hands-on when it matters.
• You bring experience across NLP, audio / music content understanding and recommender systems.
• You have a track record of managing engineering teams while delivering complex, cross-functional initiatives.
• You enjoy coaching senior and Staff engineers, and you help experienced people grow their impact.
• You have a clear, informed point of view on using AI to improve how ML engineers work, and you can inspire a team to adopt it.
• You think strategically, communicate clearly, and can balance long-term bets with near-term delivery.

Location

• This role is based in London or Stockholm.
• We offer the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

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Responsibilities:

• Own a scoped machine learning project from problem definition through experimentation, implementation, and communication of results
• Contribute to applied machine learning and AI research that informs Pinterest product experiences
• Collect, analyze, and synthesize data to develop data-driven machine learning models
• Write clean, efficient, and maintainable code
• Apply relevant machine learning techniques to modeling and ranking problems
• Break down scoped technical problems, propose approaches, and implement solutions
• Deliver assigned project milestones and share progress with teammates

Requirements:

• Working towards a Master's or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences, or related field
• Experience with machine learning, big data technologies, and scalable real-time systems
• Proficiency in at least one systems language or one ML framework
• Experience in research and solving analytical problems
• Strong communicator and team player

Benefits:

• The position is eligible for relocation assistance
• Salary range: €5,000-€6,250 EUR
• The internship dates are May 10, 2027 - August 27, 2027

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You will develop tools for building powerful, flexible, multi-modal retrievers and agents driven by Large Language Models(LLM) thereby improving the experience of millions of customers. If you're creative & passionate about solving real world conversational AI problems, come join us.

This role is pivotal in accelerating containerized pipelines for high quality multi-modal datasets and providing best-in-class retrieval efficacy. The day-to-day focus is on developing efficient, scalable systems for deduplicating, filtering, and classifying training corpora for tailored models that enhance off-the-shelf capabilities. Fundamental to these efforts are iterative testing and improvement in system cost, speed, & accuracy through micro-optimization, prompt engineering, fine tuning, and applying new research.

Responsibilities

• Develop and optimize Rust-based data processing frameworks, ensuring efficient handling of large datasets on GPU-accelerated environments, vital for LLM training.
• Lead development and iterative optimization of components for Agentic Retrieval pipelines, ensuring they demonstrate GPU acceleration & the best performing models for improved TCO.
• Collaborate with teams of LLM & ML researchers in the development of full-stack, GPU-accelerated data preparation pipelines for multimodal models Implement benchmarking, profiling, and optimization of innovative algorithms in Python in various system architectures, specifically targeting LLM applications.
• Work closely with diverse teams to understand requirements, build & evaluate POCs, and develop roadmaps for production level tools and library features within the growing LLM ecosystem.
• Build amazing products to improve employee productivity using Gen-AI & Co-pilot experiences!
• Develop integrated systems enabling unified experience across applications and driving insights for end-to-end user experience.
• Help build and maintain our Continuous Delivery pipeline with the goal of moving changes to production faster and safer, while ensuring key operational standards.
• Provide peer reviews to other specialists including feedback on performance, scalability, and correctness and actively contribute to the adoption of frameworks, standards, and new technologies

Requirements

• Bachelor’s or Master’s Degree program in Computer Science, Computer Engineering, or a related field (or equivalent experience).
• 6+ years of experience in a similar or related role
• Experience delivering software in a cloud context and is familiar with the patterns and process of managing cloud infrastructure
• Knowledge of MLOps technologies such as Docker-Compose, Containers, Kubernetes, data center deployments etc
• Excellent in-depth hands-on understanding of NLP, LLM, VLM, Generative AI, and Agentic Retrieval workflows
• Self-starter with a passion for growth, enthusiasm for continuous learning and sharing findings across the team
• Outstanding communication skills for distilling sophisticated topics down to understandable, impactful conclusions..
• Ability to work successfully with multi-functional teams, principals, and architects. Coordinates optimally across organizational boundaries and geographies.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

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You’ll work with engineering, product and data teams to prototype, evaluate and improve ML-powered features and discovery experiences.

Throughout your internship, you’ll contribute to real projects, learn how large-scale ML systems are developed and build practical skills with guidance from experienced engineers.

Responsibilities

• Own a scoped machine learning project from problem definition through experimentation, implementation and communication of results, with guidance from your manager and team
• Contribute to applied machine learning and AI research that informs Pinterest product experiences
• Collect, analyze and synthesize data to develop data-driven machine learning models
• Write clean, efficient and maintainable code, supported by appropriate documentation and testing
• Apply relevant machine learning techniques, including natural language processing or graph-based approaches, to modeling and ranking problems across discovery, ads and search
• Break down scoped technical problems, propose approaches and implement solutions with guidance from the team
• Deliver assigned project milestones, share progress and findings with teammates and seek guidance when needed

Requirements

• Working towards a Master's or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
• Experience with machine learning (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)
• Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data
• Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
• Experience in research and in solving analytical problems
• Strong communicator and team player with the ability to find solutions for open-ended problems

Benefits

• This position may be eligible for relocation assistance
• In-office collaboration 2-3 weekdays per week

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We are looking for a customer-facing solution architect to be part of the EBC team in Munich. The role involves contributing to developing technical demos and content, showcasing NVIDIA's leading-edge software and hardware technologies, and supporting the EBC team and business and technical leaders in preparing guest briefings.

Responsibilities:

• Contribute to developing technical demos and technical content
• Showcase NVIDIA's leading-edge software and hardware technologies across industries
• Support the EBC team, business and technical leaders in preparing guest briefings
• Connect NVIDIA products and GPU-accelerated solutions with guest's business
• Give onsite demos, present technical challenges, and provide guidance
• Deliver trainings, hackathons, and presentations on NVIDIA solutions and platforms
• Engage in in-depth discussions with partners and customers around NVIDIA strategies and innovations
• Ensure customer satisfaction during EBC briefings

Requirements:

• Master's degree in Computer Science, engineering, scientific computing, or related technical field, or equivalent experience
• 5+ years in AI, ML, DL, NLP, and/or Generative AI, using accelerated computing solutions
• Good programming skills (Python and/or C/C++)
• Understanding of data center components, infrastructure
• Customer-service relevant experience
• Outgoing personality, speaks with clarity and conciseness
• Strong language skills, fluent in both German and English
• Experienced speaker at international venues, conferences, workshops

Benefits:

• Opportunities for growth and development in a leading technology company
• Collaborative and dynamic work environment
• Recognition and rewards for outstanding performance

NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer.

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What is the role

The Partner Solutions Architect (PSA) is the technical force multiplier of Elastic's partner ecosystem. Where an account Solutions Architect wins one deal at a time, you build the technical capacity of a focused portfolio of partners , system integrators, resellers, MSSPs and cloud providers , so they source pipeline, win deals and deliver Elastic without us in the room.

The role rests on three pillars: drive growth with and through partners (partner-sourced pipeline); act as an architect of leverage (every partner engineer you certify adds technical selling capacity Elastic didn't have to hire); and co-innovate on repeatable solutions (joint blueprints, demos and partner-built offerings on our technology , including vertical and managed solutions Elastic doesn't build itself).

This is a pre-sales role that carries a partner-sourced pipeline target. You will work a defined portfolio of managed partners, each with an agreed technical engagement plan, and be measured on outputs , partner sourced deals, committed engagement plans and published assets , not on activity volume.

What you will be doing

Drive growth with and through partners

• Generate approved partner deal registrations, jointly owned with Partner Managers for your territory.
• Run partner-tailored sales playbooks on registered deals: qualify the deal shape, support intake and technical scoping, and help partners attach their own services , lifting average deal size.
• Accelerate partner-led opportunities, hands-on labs and proofs of concept, in collaboration with account AEs and SAs.

Architect of leverage

• Own the technical relationships and named champions at each partner in your portfolio; build trusted advisor status with senior technical stakeholders.
• Create, commit and maintain a Technical Engagement Plan per focus partner , the technical section of the Joint Business Plan , and run quarterly read-outs with the partner, Partner Manager and other stakeholders.
• Deliver enablement and certification roadmaps , workshops, hands-on labs, accreditations, certifications , that move partners up a defined technical maturity model, from first knowledge to fully autonomous, market-leading practices.

Co-innovate on repeatable solutions

• Co-architect solution blueprints and reference architectures with partners, and distributors or MSPs.
• Back partner-built solutions on Elastic , vertical, sovereign and managed offerings that extend our product surface beyond what Elastic builds itself.
• Build reusable demos, sales cards and battlecards partners can run without Elastic in the room; co-publish reference win stories and joint blog posts. Everything lands in a central repository for reuse across the ecosystem.

How success is measured

We measure this role on four transparent objectives, reviewed quarterly with evidence: approved partner-sourced deal registrations against territory targets (the headline metric); execution of the partner sales playbooks that grow average deal size; committed and reviewed Technical Engagement Plans; and published repeatable assets. Pipeline and capacity, not activity.

What you bring

• 8+ years in technology solutions, practice development, architecture or consulting in the following technological domain (Security, Observability, AI).
• 3+ years of pre-sales experience, ideally partner-facing: channel, alliances, SI/GSI or MSSP ecosystems.
• Practitioners in the following domain looking to move to a customer-facing role are encouraged to apply: (Security Analysts, SRE, DevSecOps, Developers, Search&AI practitioners)
• A one-to-many mindset: you would rather teach ten engineers to run the demo than run it ten times yourself, and you can show where your enablement turned into someone else's win.
• Working knowledge of security operations (SIEM, endpoint, cloud security) and/or observability (logging, tracing, infrastructure monitoring) , nice to have: an understanding of AI and semantic search technologies (RAG, vector search, NLP).
• Professional experience architecting or operating solutions on AWS, Google Cloud or Microsoft Azure.
• Commercial fluency: you can read a deal, talk services scope and margin with a partner, and translate technical capability into the business case their customer buys.
• Excellent communication and presentation skills for technical and non-technical audiences , a partner engineer in the morning, an architect in the afternoon, an executive sponsor in the evening.
• Comfort with ambiguity and a self-starter's bias to action; you inspire partner teams to work on what is most relevant, not just what is urgent.
• Ability to travel up to 30%, as this role requires coverage across the India region.

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 Staff Software Engineer - NLP, you will lead large cross-cutting initiatives, drive modular architecture strategy, and define standards for teams to move faster without compromising quality, security, reliability, or operability.

Responsibilities:

• Lead cross-cutting initiatives and identify systemic bottlenecks
• Drive modular backend, service, data, and platform architecture
• Define and improve standards for APIs, contracts, and testing
• Establish shared patterns for dependency management and data ownership
• Own what you build in production and participate in on-call and incident response
• Influence and partner with Product and Engineering leadership on platform roadmap decisions
• Grow engineers and mentor senior and intermediate engineers
• Lead AI adoption and define guardrails for AI-assisted workflows

Requirements:

• Cross-team technical leadership experience
• Deep backend architecture and production experience
• Experience modernizing monoliths or legacy backend architectures
• Strong ability to create alignment through clear written proposals
• Product judgment and mentorship experience
• Experience with AI-assisted or agentic engineering workflows

Preferred experience:

• Go services and APIs at scale
• Kubernetes and cloud-native operations
• Rust adoption or production ownership
• Ruby/Rails modularization and large-monolith modernization
• PostgreSQL scaling and data partitioning

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What is the role

The Partner Solutions Architect (PSA) is the technical force multiplier of Elastic's partner ecosystem. Where an account Solutions Architect wins one deal at a time, you build the technical capacity of a focused portfolio of partners , system integrators, resellers, MSSPs and cloud providers , so they source pipeline, win deals and deliver Elastic without us in the room.

The role rests on three pillars: drive growth with and through partners (partner-sourced pipeline); act as an architect of leverage (every partner engineer you certify adds technical selling capacity Elastic didn't have to hire); and co-innovate on repeatable solutions (joint blueprints, demos and partner-built offerings on our technology , including vertical and managed solutions Elastic doesn't build itself).

This is a pre-sales role that carries a partner-sourced pipeline target. You will work a defined portfolio of managed partners, each with an agreed technical engagement plan, and be measured on outputs , partner sourced deals, committed engagement plans and published assets , not on activity volume.

What you will be doing

Drive growth with and through partners

• Generate approved partner deal registrations, jointly owned with Partner Managers for your territory.
• Run partner-tailored sales playbooks on registered deals: qualify the deal shape, support intake and technical scoping, and help partners attach their own services , lifting average deal size.
• Accelerate partner-led opportunities, hands-on labs and proofs of concept, in collaboration with account AEs and SAs.

Architect of leverage

• Own the technical relationships and named champions at each partner in your portfolio; build trusted advisor status with senior technical stakeholders.
• Create, commit and maintain a Technical Engagement Plan per focus partner , the technical section of the Joint Business Plan , and run quarterly read-outs with the partner, Partner Manager and other stakeholders.
• Deliver enablement and certification roadmaps , workshops, hands-on labs, accreditations, certifications , that move partners up a defined technical maturity model, from first knowledge to fully autonomous, market-leading practices.

Co-innovate on repeatable solutions

• Co-architect solution blueprints and reference architectures with partners, and distributors or MSPs.
• Back partner-built solutions on Elastic , vertical, sovereign and managed offerings that extend our product surface beyond what Elastic builds itself.
• Build reusable demos, sales cards and battlecards partners can run without Elastic in the room; co-publish reference win stories and joint blog posts. Everything lands in a central repository for reuse across the ecosystem.

How success is measured

We measure this role on four transparent objectives, reviewed quarterly with evidence: approved partner-sourced deal registrations against territory targets (the headline metric); execution of the partner sales playbooks that grow average deal size; committed and reviewed Technical Engagement Plans; and published repeatable assets. Pipeline and capacity, not activity.

What you bring

• 5+ years in technology solutions, practice development, architecture or consulting in the following technological domain (Security, Observability, AI).
• 2+ years of pre-sales experience will be prioritised but not required, ideally partner-facing: channel, alliances, SI/GSI or MSSP ecosystems.
• Professional fluency in English and another ASEAN language (Bahasa, Thai, Vietnamese) will be a big plus, as you'll be working directly with customers in ASEAN and collaborating with global teams.
• Practitioners in the following domain looking to move to a customer-facing role are encouraged to apply: (Security Analysts, SRE, DevSecOps, Developers, Search&AI practitioners)
• A one-to-many mindset: you would rather teach ten engineers to run the demo than run it ten times yourself, and you can show where your enablement turned into someone else's win.
• Working knowledge of security operations (SIEM, endpoint, cloud security) and/or observability (logging, tracing, infrastructure monitoring) , nice to have: an understanding of AI and semantic search technologies (RAG, vector search, NLP).
• Professional experience architecting or operating solutions on AWS, Google Cloud or Microsoft Azure.
• Commercial fluency: you can read a deal, talk services scope and margin with a partner, and translate technical capability into the business case their customer buys.
• Excellent communication and presentation skills for technical and non-technical audiences , a partner engineer in the morning, an architect in the afternoon, an executive sponsor in the evening.
• Comfort with ambiguity and a self-starter's bias to action; you inspire partner teams to work on what is most relevant, not just what is urgent.
• Ability to travel up to 40%, as this role is based in Singapore and requires coverage across the ASEAN region.

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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At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.”

Staff Machine Learning Engineer, Identity Verification

As a Staff Machine Learning Engineer on the Identity Verification team within the Platform group, you'll own the ML systems that determine whether a person, document, and capture session are legitimate. Every signup, account recovery, and high-risk action at Coinbase depends on these models. You'll lead the technical strategy for IDV ML end-to-end, from architecture through production enforcement, protecting the integrity of millions of accounts.

Responsibilities

• Own the full IDV ML stack, including document authenticity models, 1:1 and 1:N face-match, liveness detection, presentation-attack detection, and deepfake/injection detection from feature pipeline through threshold tuning and production enforcement.
• Build identity-graph systems using GNNs that cluster accounts sharing biometric, device, and document signals to detect synthetic-identity rings and coordinated fraud at onboarding.
• Develop behavioral and device-intelligence models for capture-session anomaly detection, bot-vs-human classification, and device-fingerprint-based risk scoring at real-time latency.
• Drive vendor ML strategy by benchmarking external models against a Coinbase-owned evaluation set, designing dynamic routing logic across providers and geographies, and building the in-house evaluation layer that catches regressions before they reach users.
• Lead and mentor senior and mid-level engineers in the pod while partnering with ML Platform and Risk ML teams to align cross-company ML system design.

Required Skills and Experience

• 8+ years deploying production ML systems at scale, with proven technical leadership owning cross-team ML architecture from design through production.
• Domain experience in identity verification, biometrics, or account integrity with deep applied ML in at least two of: computer vision/biometrics, GNNs, sequence models, or NLP/LLMs.
• Expert-level Python with production experience in TensorFlow or PyTorch, including model training, evaluation, and serving infrastructure.
• Track record translating KYC/AML requirements and fraud trends into ML roadmaps and communicating trade-offs to Product, Compliance, Risk, and Security stakeholders.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Pay Information

• Annual base salary range (excluding equity and bonus): $218,025-$256,500 USD

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You will own the intelligence layer that makes AIDA's agents correct, trustworthy, and measurably better over time. The work spans information retrieval, knowledge representation, and evaluation science. You will turn ambiguous notions of "agent quality" into clear metrics, build the grounding systems that connect agent reasoning to verified data, and create the evaluation infrastructure that makes quality a first-class engineering discipline.

You will operate at the research/systems boundary: running experiments with academic rigor and shipping results with production engineering discipline. Research and engineering are not separate tracks here. You will own experiments end to end, from hypothesis through production deployment.

As an Applied AI Research Engineer at Starburst, you will:

• Design and build grounding systems that connect agent reasoning to verified enterprise data sources

• Build and optimize retrieval pipelines (RAG, hybrid search, structured query generation) for accuracy and latency

• Define data representation strategies that preserve semantic fidelity across heterogeneous enterprise data (catalogs, schemas, lineage)

• Create evaluation frameworks: automated benchmarks, regression suites, human evaluation protocols

• Convert validated research findings into production systems that ship to users

• Establish quality metrics and dashboards that track agent correctness week over week

• Build feedback loops where user interaction data flows back into evaluation datasets and informs grounding improvements

Requirements

• 3+ years of experience in information retrieval, NLP, knowledge representation, or applied ML research

• Production experience building RAG, grounding, or retrieval systems (not prototypes or demos)

• Strong evaluation methodology: benchmark design, statistical analysis, reproducible experiments

• Comfort operating at the research/systems boundary: you read papers and you ship code

• Python fluency; experience with vector databases, embedding models, LLM APIs

• Track record of converting research insights into shipped production systems

Preferred Qualifications

• Experience with enterprise data systems (SQL engines, data catalogs, schema metadata)

• Familiarity with text-to-SQL or structured query generation

• Published research or open-source contributions in IR, NLP, or evaluation methodology

• Experience designing evaluation pipelines that run in CI/CD

• Familiarity with JVM-based systems

• Ability to Travel: This role will require 25% in-person travel for purposes including but not limited to new hire onboarding, team and department offsites, customer engagements, and other company events. Actual travel expectations may vary by role and business needs.

Salary Information

The salary range provided for this role reflects the minimum and maximum targets for candidates across all U.S. locations and could be inclusive of variable compensation, such as commission or bonus. All employees receive equity packages (ISOs) and have access to a comprehensive benefits offering. Actual compensation packages are determined based on relevant skills, experience, education and training, and specific work location.

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The team is passionate about research that pushes boundaries and builds the next generation of generative models with real-world impact, particularly in advancing fundamental generative AI methods for image, video, 3D, graph, and multimodal data.

Responsibilities:

• Research, design, and implement novel, large-scale generative AI methods.
• Publish original research.
• Collaborate with other team members, research teams, and product teams.
• Transfer technology to product groups.

Requirements:

• Pursuing a Ph.D. program in Computer Science/Engineering, Statistics, Electrical Engineering, etc.
• Strong publication record at top ML venues such as NeurIPS, CVPR, ICLR, ICML, ICCV, ECCV, etc.
• Excellent knowledge of the theory and practice of generative AI.
• Strong knowledge of computer vision, 3D, NLP, or AI for science.
• Excellent programming skills in rapid prototyping environments such as Python; C++ and parallel programming (e.g., CUDA) is a plus.
• Knowledge of common deep learning frameworks, such as PyTorch.
• Excellent communication skills.

Benefits:

• Competitive salaries
• Generous benefits package
• Intern hourly rates: 38 USD - 94 USD

Applications for this job will be accepted until September 19, 2026.

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The AI Security team within Reddit’s Security Platform Engineering organization builds security into Reddit’s products, engineering systems, AI platforms, and operational infrastructure so the secure path is the easiest path for both people and agents.

As a founding Staff Machine Learning Engineer, you will lead the development, training, and optimization of models for AI security at Reddit. This is a strategic and hands-on individual contributor role, combining deep ownership of model architecture, training data, and experimentation with technical leadership across teams.

Responsibilities

• Select, adapt, fine-tune, evaluate, and deploy pretrained models and lightweight classifiers for Reddit-specific security problems.
• Build reproducible training and evaluation pipelines on Reddit’s ML platform, partnering with platform engineers to improve inference performance, resource efficiency, and operational reliability.
• Set the technical vision and multi-quarter modeling roadmap, partnering with cross-functional teams to gather requirements, define model architectures, and iterate on model development.
• Conduct model evaluations and performance analysis to improve accuracy and adversarial robustness, and define launch criteria that balance false positives, latency, throughput, reliability, and cost.
• Own training-data quality and the production model lifecycle, using monitoring, incident findings, and red-team feedback to guide dataset improvements, retraining, and safe rollout or rollback.
• Establish best practices for responsible ML development and deployment, including reproducible experiments, testing, model and data lineage, and privacy-aware data use.
• Stay current with research in NLP, large language models, and relevant multimodal techniques, translating promising advances into measurable model improvements.
• Mentor engineers and lead technical discussions and reviews, shaping the team’s long-term ML capabilities and AI security direction.

Requirements

• 8+ years of experience developing machine learning models, with substantial hands-on model training experience, demonstrated production impact, and a record of leading complex initiatives across teams.
• Strong background in Python programming, software engineering, and deep learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.
• Deep understanding of neural network architectures and optimization, with proficiency in data preprocessing, tokenization, embeddings, language modeling, and model calibration.
• Expertise in scalable data pipelines and distributed training frameworks such as Ray Train or PyTorch Distributed, with a strong understanding of hardware and system tradeoffs.
• Demonstrated rigor in experimental design and model evaluation, including representative holdouts, ablation studies, adversarial tests, and error analysis to diagnose training issues, bias, and generalization gaps.
• Excellent written and verbal communication, with the ability to explain model behavior, security risk, uncertainty, and tradeoffs to technical and non-technical partners.

Preferred Qualifications

• Experience applying ML to security, trust and safety, fraud, privacy, or related adversarial domains.
• Experience with adversarial training, model distillation, active learning, or synthetic-data generation to improve model quality, robustness, and training efficiency.

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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Our Public Sector Machine Learning team focuses on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. The team's primary focus is on agentic systems built on large language models, developing agents that solve complex operational and planning challenges for government partners.

As a Senior MLE, you'll have design authority over a capability area, proposing architectures, building them with support from other engineers, and being accountable for their performance in customer-dependent environments.

Responsibilities:

• Own the design and delivery of agent capabilities end-to-end, including architecture, implementation, and evaluation.
• Define new patterns in problem spaces with no established approach and lead the work to build them.
• Put state-of-the-art models into production to solve customer problems.
• Improve and maintain production models and agents through retraining, hyperparameter tuning, and architectural updates.
• Build agent-level evaluation benchmarks and use them to improve performance.
• Partner with product and research teams to scope high-impact initiatives.
• Build scalable machine learning infrastructure.
• Work directly with government users and subject-matter experts.
• Act as a force multiplier and primary reviewer for your team.
• Communicate technical tradeoffs clearly to non-technical stakeholders.
• Treat security and compliance as design constraints.
• Serve as a cross-functional representative for machine learning techniques.
• Be comfortable learning new technologies quickly and managing multiple priorities.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

Requirements:

• Active TS security clearance.
• 5+ years of experience building and deploying applied ML systems in production environments.
• Extensive experience with GenAI, Agentic AI, NLP, deep learning, and computer vision.
• Track record of owning architectural decisions.
• Experience shipping agentic systems with real production traffic.
• Solid background in algorithms, data structures, and object-oriented programming.
• Strong programming skills in Python, experience in PyTorch or Tensorflow.
• Experience mentoring or reviewing engineers.

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning, or Artificial Intelligence.
• Experience working with cloud platforms and deploying ML models in cloud environments.
• Experience with computer vision, generative AI models, large language models, or agentic systems.
• Familiarity with ML evaluation frameworks and agentic model design.
• Experience deploying ML in classified environments.
• Geospatial or GEOINT experience.
• Inference optimization experience.
• Fine-tuning experience.

Benefits:

• Comprehensive health, dental, and vision coverage.
• Retirement benefits.
• Learning and development stipend.
• Generous PTO.
• Commuter stipend.

Salary Range:

The base salary range for this full-time position in Washington DC is: $235,200-$294,000 USD

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As a Search Solutions Architect, you'll design and deploy innovative solutions that leverage AI, semantic search, and vector search technologies, helping organisations unlock their data's full potential.

Responsibilities

• Architect next-generation search solutions using Elastic's latest AI, semantic search, and vector search advancements.
• Harness the power of ELSER to achieve unparalleled accuracy and efficiency in search queries and data retrieval.
• Unlock the potential of vector search by implementing powerful algorithms and creating high-dimensional vector representations of data.
• Deliver compelling proof-of-concept projects showcasing the combined value of AI, semantic search, vector search, and ELSER.
• Become a thought leader and evangelize Elastic's vision through presentations, blog posts, and other channels.
• Cultivate a collaborative environment within the Solutions Architecture team and across departments.
• Collaborate with engineers, data scientists, and industry experts who are passionate about pushing the boundaries of search and AI-powered solutions.
• Continuously learn and explore the evolving landscapes of AI, semantic search, vector search, and related technologies.

Requirements

• 10+ years of experience in designing and architecting enterprise-level search solutions.
• Deep understanding of AI and semantic search technologies, with a focus on RAG, vector search, and NLP.
• Familiarity with emerging ecosystem technologies like Hugging Face, Langchain, etc.
• Solid knowledge of ELSER's architecture and other models within the Hugging Face ecosystem.
• Experience with vector databases and other emerging data storage solutions.
• Excellent communication and presentation skills.
• Collaborative spirit and a passion for sharing knowledge and expertise.
• Unwavering commitment to continuous learning and staying ahead of the curve in technology and emerging trends.
• Fluency in Spanish would be an advantage.

Benefits

• Competitive pay
• Health coverage for you and your family
• Flexible locations and schedules
• Generous number of vacation days
• Matching up to $2000 for financial donations and service
• Up to 40 hours each year for volunteer projects
• Minimum of 16 weeks of parental leave

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Key Responsibilities:

• Develop and deploy ML-based search and discovery relevance models and systems integrated with Databricks' products and services.
• Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation.
• Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
• Contribute to building a robust framework for evaluating search ranking improvements, both offline and online.

Requirements:

• BS+ (M.S. or PhD preferred) in Computer Science or a related field.
• 10+ years of experience developing search relevance systems at scale in production or high-impact research environments.
• Experience applying Large Language Models (LLMs) to search relevance.
• Experience in one or more of the following areas: query understanding, NLP, text mining, recommendations, personalization, discovery, conversational AI.
• Strong understanding of computer science fundamentals.
• Contributions to well-used open-source projects.

Benefits: At Databricks, we offer comprehensive benefits and perks that meet the needs of all our employees. You can find specific details on the benefits offered in your region [here](link).

About Databricks: Databricks is the Data and AI company. More than 20,000 organizations worldwide , including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 , rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents.

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You will leverage the full NVIDIA software & hardware ecosystem to design, optimize, and deliver production-grade generative AI solutions for enterprise customers.

What You'll Be Doing:

• Conduct in-depth analysis of customers' latest needs and co-develop accelerated computing solutions with key customers.
• Assist in supporting industry accounts and driving research/influencing/new business in those accounts.
• Deliver technical projects, demos and client support tasks as directed by the Solution Architecture leadership team.
• Understand and analyze Top AI Labs customers' workloads and demands for accelerated computing, including but not limited to: LLM training/inference acceleration and optimization, application optimization for Agent AI/RAG, kernel analysis, etc.
• Assist Top AI Laps customers in onboarding NVIDIA's software and hardware products and solutions, including but not limited to: CUDA, TensorRT-LLM, NeMo Framework, etc.
• Be an industry thought leader on integrating NVIDIA technology into applications built on Deep Learning, High Performance Data Analytics, Robotics, Signal Processing and other key applications.
• Be an internal champion for Data Analytics, Machine Learning, and Cyber among the NVIDIA technical community.

What We Need To See:

• 3+ years’ experience with research/development/application of Machine Learning, data analytics, or computer vision work flows.
• Outstanding verbal and written communication skills. Ability to work independently with minimal day-to-day direction.
• Knowledge of industry application hotspots and trends in AI and large models.
• Familiarity with large model-related technology stacks and common inference/training optimization methods. C/C++/Python programming experience.
• Desire to be involved in multiple diverse and innovative projects.
• Experience using scale-out cloud and/or HPC architectures for parallel programming.
• MS or PhD in Engineering, Mathematics, Physics, Computer Science, Data Science, Neuroscience, Experimental Psychology or equivalent experience.

Ways To Stand Out From The Crowd:

• AIGC/LLM/NLP experience.
• CUDA optimization experience.
• Experience with Deep Learning frameworks and tools.
• Engineering experience in areas such as model acceleration and kernel optimization.
• Extensive experience designing and deploying large scale HPC and enterprise computing systems.

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Who we are

Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale , building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants.

Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers.

What you'll do

We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem.

Responsibilities

• Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections
• Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions
• Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy
• Develop pipelines and automated processes to train and evaluate models in offline and online environments
• Integrate ML models into production systems and ensure their scalability and reliability
• Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers
• Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions
• Mentor engineers and contribute to a strong ML engineering culture within the team

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 10+ years of industry experience building and shipping ML systems in production
• Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
• Hands-on experience in designing, training, and evaluating machine learning models
• Hands-on experience in productionizing and deploying models at scale
• Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets
• Strong collaboration skills and the ability to work across teams and contribute to peers' success
• Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset

Preferred qualifications

• MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science)
• Experience in fintech, open banking, or financial data domains
• Experience with NLP, LLMs, or text classification at scale
• Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality
• Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
• Experience with deep learning architectures, including transformers

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We are seeking a Staff Gen AI Research Scientist to serve as a founding ML expert on our team. In this role, you will design, fine-tune, and deploy the next generation of generative AI, LLMs, and agentic systems that power our air-dominance platforms and collaborative autonomous behaviours.

Responsibilities

• Build & Fine-Tune Core Models: Develop, pre-train, and fine-tune in-house LLMs and multimodal foundation models.
• Deploy at the Edge: Architect and optimise models to run directly on tactical edge compute and power-constrained hardware onboard physical assets.
• Develop Agentic Workflows: Design and implement robust agentic architectures, multi-agent coordination frameworks, and planning loops for complex, multi-domain military missions.
• Drive Multimodal Sensor Integration: Collaborate closely with computer vision, perception, and motion planning teams to build systems capable of reasoning over diverse modalities.
• Shape Data & Evaluation Strategies: Define and execute data collection strategies across physical assets, test sites, and virtual simulations.
• Rapid Prototyping to Production: Build early-stage prototypes alongside customers, quickly iterate on feedback, and scale those prototypes into production-grade features deployed across our family of systems.

Requirements

• Core Engineering Strength: Strong production-level coding skills in Python and deep learning frameworks (like PyTorch or JAX).
• ML & LLM Expertise: Hands-on experience training, fine-tuning, and evaluating LLMs, Generative AI, or multimodal models.
• Solid Foundation: A strong background in a classical technical discipline (Computer Vision, NLP, Robotics, or Speech) with 2+ years of dedicated experience focusing on generative models and modern transformer architectures.
• Tooling Familiarity: Experience using modern model training, alignment, and orchestration tools (e.g., Axolotl, Hugging Face, DeepSpeed, Megatron-LM, LangChain, or LlamaIndex).
• Problem-Solving & Adaptability: Ability to operate comfortably in a fast-paced environment, moving from ambiguous mission requirements to concrete code and functional prototypes.
• Academic/Industry Background: Degree (B.S., M.S., or Ph.D.) in Computer Science, Machine Learning, Robotics, Physics, Mathematics, or a related technical field.
• Clearance Eligibility: Eligible to obtain and maintain an active U.S. Top Secret security clearance.

Benefits

• Highly competitive equity grants
• Top-tier benefits for full-time employees, including comprehensive health, recovery, and support
• Salary range: $220,000-$292,000 USD

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As a team, we play a central role in discovery, crawling, selection, document processing and aggregation of web graph. Work in our team is unique in that you’ll be able to work with trillions of documents, which pushes the boundaries of our infrastructure and computational resources. Moreover, it provides interesting challenges that haven’t been encountered before, thereby combining research and product applications.

We are looking for the best and brightest to join our team and help us apply the next generation of LLM, NLP, deep learning, large-scale distributed systems, and informational retrieval techniques. As a Senior Product Manager, you will build the next generation of Unified Index Platform (UIP), which is being used for featurizing, filtering, and indexing for Search, Grounding, and Training. UIP supports all data types and structured data across web, PDF, image, video, local, shopping, etc. The candidate will focus on defining KPIs for success, doing deep data analysis, writing product specifications, and delivering complex large projects.

This is a highly collaborative, cross-functional role ideal for someone who thrives in operational complexity, is deeply organised, and loves working at the intersection of people, process, and product quality. You will partner closely with engineering, research, design, data science, legal, privacy, and security teams to build compliant, scalable, high-quality AI systems that serve hundreds of millions of users.

Responsibilities

• Strategy & Vision
• Define the multi-year product vision and strategy for Microsoft AI Experience Index Pipelines, related success metrics, feedback loops, and testing mechanisms.
• Create strategies for processing all kinds of entities/data types, real-time injection of critical data at scale, and on-boarding different teams that want to process and build large-scale indexes.

• Execution & Delivery
• Define success criteria, OKRs, and KPIs across multiple product surfaces and organisational boundaries.
• Establish methodologies for on-boarding SLM/LLMs and platform support for large-scale publish-subscribe mechanisms.
• Drive platform architecture decisions in partnership with engineering and research teams.
• Plan, size, and continuously optimise capacity (compute, storage, data throughput, and cost) to build, scale, and operate training pipelines reliably.

• Cross-Functional Leadership
• Partner with Legal, Privacy, Security, HR, and Compliance teams to ensure platforms are compliant-by-design.
• Collaborate with teams across Microsoft that need to process, build, or access very large indexes for Search or AI scenarios.
• Define and evangelise processes for developers and researchers to improve labelling workflows, model-as-a-judge systems, and agentic evaluation pipelines.

• Innovation & Technical Depth
• Research, design, and operationalise new evaluation techniques and metrics that improve the quality and reliability of LLM and ML-based products.
• Go deep into new technical areas quickly and provide clarity to engineering teams on requirements, architecture, and trade-offs.

Qualifications

Required Qualifications:

• Bachelor’s Degree AND 5+ years experience in product/service/program management or software development
• OR equivalent experience.

Preferred Qualifications:

• Bachelor’s Degree AND 8+ years experience in product/service/program management or software development
• OR equivalent experience.
• 4+ years experience managing cross-functional and/or cross-team projects.
• Outstanding analytical skills
• Experience in writing good detailed technical specifications for all new features
• Good understanding of LLM, Big Data, Machine Learning, and Data Mining.
• Understanding of Security, Privacy, and Compliance practices.
• Attention to detail, planning, organisation, and project management skills
• Ability to quickly adapt to new technology and go deep in new focus areas.
• Excellent oral and written communication skills with the ability to collaborate effectively with a large number of partners and customers and translate requirements into a product vision and roadmap.

Benefits

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

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Key Responsibilities:

• Develop and execute test plans for NVIDIA HGX/DGX/MGX platforms on servers, OS, FW, and CUDA SW stack
• Install and test various systems OS, server firmware, and SW stack
• Drive support for root cause analysis on reliability and validation test failures
• Build, develop, and debug server and OS level automation front-end and back-end framework and tests
• Review partner and supplier test results and prescribe additional reliability testing as needed
• Work in an agile software development team with high production quality standards
• Manage bug lifecycle and collaborate with inter-groups to drive solutions

Requirements:

• Bachelor's Degree in a STEM field
• 5+ years of proven experience or master's degree
• Proven experience in OS and server level automation, CI/CD process, and DevOps using Python, SHELL, Ansible, Jenkins, C/C++, Java, JavaScript
• Strong server and Linux troubleshooting and debugging experience
• Good knowledge and hands-on experience in model testing, AI tools/frameworks, NLP, and LLM benchmarking
• Experience in using AI development tools for test plans creation, test cases development, and test cases automation

Preferred Qualifications:

• Experience with AI-related tools, LLM, and NLP
• Experience working with NVIDIA GPU hardware
• Solid understanding of virtualization in Linux (KVM, Docker orchestrated with Kubernetes)
• Background in parallel programming, ideally CUDA/OpenCL

Benefits:

• Equity eligibility
• Benefits package

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Responsibilities:

• Develop and execute test plans for NVIDIA HGX/DGX/MGX platforms on servers, OS, FW, and CUDA SW stack
• Install and test various systems OS, server firmware, and SW stack
• Drive support for root cause analysis on reliability and validation test failures
• Build, develop, and debug server and OS level automation front-end and back-end framework and tests
• Review partner and supplier test results and prescribe additional reliability testing as needed
• Work in an agile software development team with high production quality standards
• Manage bug lifecycle and collaborate with inter-groups to drive solutions

Requirements:

• Bachelor's Degree in a STEM field
• 5+ years of proven experience; or master's degree
• Proven experience with OS and server level automation, CI/CD process, and DevOps using Python, SHELL, Ansible, Jenkins, C/C++, Java, JavaScript
• Strong server and Linux troubleshooting and debugging experience
• Good knowledge and hands-on experience with model testing, AI tools/frameworks, NLP, and LLM benchmarking
• Experience with AI development tools for test plans creation, test cases development, and test cases automation

Preferred Qualifications:

• Experience with NVIDIA GPU hardware
• Solid understanding of virtualization in Linux (KVM, Docker orchestrated with Kubernetes)
• Background in parallel programming, ideally CUDA/OpenCL

Benefits:

• Equity
• Other benefits

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Responsibilities:

• Design NLP/LLM/GenAI applications/products by following robust coding practices
• Explore SoTA models/techniques for automotive industry use cases
• Conduct ML experiments to train/infer models
• Deploy REST APIs or minimalistic UIs for NLP applications using Docker and Kubernetes tools
• Showcase NLP/LLM/GenAI applications to users through web frameworks
• Converge multibots into super apps using LLMs with multimodalities
• Develop agentic workflows using Autogen, Agentbuilder, langgraph
• Build modular AI/ML products that can be consumed at scale

Qualifications:

• Bachelor's or Master's Degree in Computer Science, Engineering, Maths or Science
• Performed modern NLP/LLM courses/open competitions
• Strong communication skills and excellent teamwork
• Experience in LLM models, Gen AI model development, and multimodalities
• Implemented real-world chatbots or conversational agents at scale
• Experience in developing image generation/translation tools using latent diffusion models
• Expertise in handling large-scale structured and unstructured data

Requirements:

• High familiarity with DL theory/practices in NLP applications
• Comfort level with coding in ADK, A2A, AgentSkills, Ontology, Huggingface, LangGraph, LangChain, Chainlit, Tensorflow, Pytorch, Scikit-learn, Numpy, and Pandas
• Knowledge in fundamental text data processing
• Implemented real-world BERT/other transformer fine-tuned models
• Familiarity with Docker tools, pipenv/conda/poetry env, and Github
• Use of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI
• Experience in using GPU/CPU of cloud and on-prem infrastructures
• Skillset to leverage cloud platform for Data Engineering, Big Data, and ML needs
• Use of Dockers, orchestration tools, and CI/CD infrastructure as code tools
• Good UI skills to visualize and build better applications

Benefits: No benefits mentioned

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Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead.

Risk Engineering builds the systems that protect Stripe from fraud, credit losses, and regulatory risk across user onboarding, compliance, financial crimes, and identity verification. Financial Crimes Engineering builds the systems and controls that let Stripe safely onboard businesses in emerging and regulated industries while keeping Stripe in compliance with sanctions law, anti-money-laundering rules, and financial partner requirements.

As a Staff Engineer on Financial Crimes Engineering, you’ll own technical direction across our screening, detection, and review systems. You’ll design infrastructure that balances screening latency with accuracy, build the systems that surface emerging risk and collect information to resolve it, and ensure investigators have the tooling they need.

Responsibilities

• Define technical strategy & vision: Establish the multi-year architecture for Stripe’s real-time entity matching infrastructure, policy enforcement engines, and reviewer tooling
• Architect high-scale systems: Design and optimize distributed screening engines and detection pipelines handling entity- and transaction-level requests at scale with strict SLA and uptime guarantees
• Build investigations infrastructure: Design the systems that surface suspicious activity, collect additional information from users, route cases to reviewers, and augment human judgment with AI to drive faster, more accurate case resolution
• Drive cross-functional technical alignment: Serve as the primary technical voice with Legal, Compliance, ML, and Product leaders to translate evolving global regulatory requirements into technical ones
• Enable Risk as a Service: Lead the architectural evolution toward a multi-tenant screening platform - laying the technical foundation for both internal product launches and external developer-facing APIs
• Raise the engineering bar: Set standards for system design, API consistency, and operational rigor across backend and ML-adjacent systems; mentor senior engineers and lead high-stakes design reviews
• Ensure operational excellence: Own system reliability, observability, and latency, ensuring production systems adapt quickly to sudden changes in global sanctions lists or traffic spikes, and participate in an on-call rotation for these systems

Requirements

• 10+ years of software engineering experience, including 5+ years in a strategic technical leadership role on core infrastructure, distributed systems, or platform engineering
• Proven experience designing, building, and operating high-throughput, low-latency APIs and distributed backend systems at scale
• Track record of defining technical roadmaps and driving alignment across multiple engineering teams and cross-functional domains
• Hands-on technical depth - comfortable jumping from high-level system architecture down to code-level performance tuning and production debugging
• Exceptional written and verbal communication skills, with a track record of articulating technical decisions to non-technical stakeholders

Preferred Qualifications

• Domain experience in financial crimes, sanctions screening, AML (Anti-Money Laundering), entity resolution, or high-stakes trust & safety platforms
• Familiarity with applying LLMs or ML to operational workflows (e.g., case triage, entity resolution, review automation), or experience with fuzzy string matching and NLP for entity matching
• Experience building investigator/analyst tooling, case management systems, or human-in-the-loop review workflows
• Experience building platform products or APIs consumed by both internal engineering teams and external developers (e.g., API backward compatibility, client libraries)
• Prior experience leading 0-1 platform builds or major architectural modernizations from concept to production
• Tech stack experience across multi-language environments (e.g., Primarily Ruby and Java) and modern data infrastructure
• Comfortable navigating ambiguity and making high-judgment engineering calls in a fast-moving regulatory environment

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As Principal Product Manager for the AI-First Service Platform, you'll own core capabilities of the intelligence layer and surface area experience behind Gusto's customer-facing AI help/service.

Responsibilities:

• Own core capabilities of Gusto's AI service intelligence layer
• Build and refine models and frameworks that decode what customers need
• Own the evaluation layer for the systems you ship
• Experiment, learn, iterate, and ship impactful solutions rapidly
• Identify and dismantle legacy systems and hard-coded workflows that create friction
• Collaborate with GTM and Service teams
• Define and track metrics that matter

Requirements:

• 8+ years of product management experience building complex, data-intensive platforms
• Deep familiarity with AI/ML concepts
• Hands-on builder who can move fluidly between strategy and execution
• Strong cross-functional instincts
• Bias toward simplicity

Benefits:

• Competitive base pay
• Benefits
• Equity (RSUs)

Locations:

• Denver, CO
• San Francisco, CA
• New York, NY

Salary: $179,000/yr to $220,000/yr in Denver & most major metro locations $210,000/yr to $260,000/yr for San Francisco, Seattle & New York

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As Principal Product Manager for the AI-First Service Platform, you'll own core capabilities of the intelligence layer and surface area experience behind Gusto's customer-facing AI help/service.

Responsibilities:

• Own core capabilities of Gusto's AI service intelligence layer
• Build and refine models and frameworks that decode what customers need
• Own the evaluation layer for the systems you ship
• Experiment, learn, iterate, and ship impactful solutions rapidly
• Identify and dismantle legacy systems and hard-coded workflows that create friction
• Collaborate with GTM and Service teams
• Define and track metrics that matter

Requirements:

• 8+ years of product management experience building complex, data-intensive platforms
• Deep familiarity with AI/ML concepts
• Hands-on builder who can move fluidly between strategy and execution
• Strong cross-functional instincts
• Bias toward simplicity

Benefits:

• Competitive base pay
• Benefits
• Equity (RSUs)

Locations:

• Denver, CO
• San Francisco, CA
• New York, NY

Salary: $179,000/yr to $220,000/yr in Denver & most major metro locations $210,000/yr to $260,000/yr for San Francisco, Seattle & New York

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The Partner Solutions Architect is a high-priority role that will have a sizable, immediate impact on Elastic and our entire partner ecosystem. In this role, you will be responsible for the technical management, activation and enablement of our partner relationships.

Responsibilities

• Acting as a strategic technical leader in the field - you are a self starter. You lead the lifecycle of agreements from business case, planning and governance, technical architecture, solution build, business modeling, and ensure business case is delivered to the end customer through partners.
• Translating technical capabilities into business benefits - you are instrumental in showing how Elastic’s offerings can be tailored to address the specific needs of the partner’s customer base, thereby making the solutions more relatable and easier to sell.
• Support partners in developing proactive proposals in key accounts for growing Elastic Cloud with partners.
• Architecting custom solutions - By co-creating tailored solutions that integrate Elastic’s technology within the partner’s offerings or customer environments, Partner Solution Architects ensure that partners are not just enabled but are fully equipped to solve unique customer challenges, which in turn, stimulates active selling.
• Co-Leading design wins - you work collaboratively on design wins, meaning they assist in developing the technical blueprint for industry-specific solutions that partners can use in their go-to-market strategies, effectively boosting activation.
• Driving technical advocacy and earning trust - you foster a deeper technical rapport with the partner, aiding their ability to comprehend and advocate for Elastic solutions, which is a key component for partner activation.
• Build trusted relationships with senior technical individuals.
• Supporting co-marketing efforts - you provide the technical backbone to marketing campaigns, ensuring that the partner’s messaging accurately reflects the capabilities and benefits of Elastic’s offerings, which is important for an effective activation campaign.
• Authoring customer-facing publications - you stay on top of market trends around cloud and industry best practices, building deep relationships with technical leaders inside and outside of the company, contributing to publications, white papers, blogs, and sales collateral.
• Build community to drive growth - develop a focused and structured network of Solutions Architects who can pinpoint opportunities based on the synergies between Elastic and partners.

Requirements

• 8+ years experience in technology solutions, practice development, architecture, consulting, and/or cloud/infrastructure technologies.
• 5+ years of experience working with multinational customers in support of technology sales.
• Previous PreSales experience within the software sector.
• An understanding of AI and semantic search and vector technologies, with a focus on RAG, vector search, and NLP.
• Professional experience architecting/operating solutions built on AWS, Google Cloud or MSFT Azure.
• Excellent communication and presentation skills, able to engage both technical and non-technical audiences.

Compensation

• The typical starting salary range for this role is: $107,500-$170,100 USD
• The typical starting Target Variable range for this role is: $46,000-$72,800 USD
• The typical starting On-Target Earnings (OTE) range for this role is: $153,500-$242,900 USD

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You will plan and advance strategic marketing campaigns that boost the adoption of NVIDIA's data processing software and hardware platforms.

Responsibilities:

• Lead positioning, messaging, and launches for unstructured data processing and decision optimization products
• Drive content and messaging for executive keynotes
• Capture practitioner feedback and test messaging through community activities such as live-streams, hackathons, and meetups
• Carry out ecosystem analysis to guide our positioning and assist campaigns that convey our key messages
• Develop technical content including blogs and videos to support technical practitioners in using NVIDIA's SDKs
• Develop presentations aimed at educating industry and business analysts and PR on our positioning and value proposition

Requirements:

• 8+ years' experience with launching, go-to-market, and scaling technical product adoption for developers, data scientists, or operations research practitioners
• Bachelor's degree in a scientific or technical field (or equivalent experience)
• Understanding of unstructured data processing or optimization and operations research environments
• Familiarity with ML, vector search, or key solver technologies
• Comfortable explaining Python code used in presentations, demos, and articles

Nice to Have:

• Clearly convey your comprehension of NVIDIA's approach and technology for data processing
• Background in NLP, AI/LLM Ops, operations research, logistics/routing, supply chain, or as a quantitative analyst
• Growing portfolio of blogs and social posts with links to your relevant work, including technical writing, social media presence, videos, and research papers
• Building and nurturing technical open source communities

We offer highly competitive salaries and a full benefits package.

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You will lead the RWD Informatics team in transforming diverse real-world datasets into regulatory-grade, research-ready assets.

Your efforts will contribute to developing and commercializing early cancer detection products and digital health solutions.

The role reports to the Chief Product Officer and is a remote position.

Responsibilities:

• Lead the operations and continuous improvement of the RWD curation pipeline.
• Evaluate and integrate agentic workflows and AI to ensure accuracy and quality.
• Design and implement processes, analytical methods, and AI tools to integrate various RWD sources.
• Prepare data for analysis, including data cleaning and mapping.
• Ensure 'regulatory-grade' data quality where required.
• Develop a scalable unstructured data curation operation.
• Create a comprehensive, multi-year strategy for the acquisition, management, curation, and use of RWD.
• Architect, build, and maintain a robust RWD platform and pipeline.
• Lead clinical data analyses to support product strategy, development, and commercialization.
• Serve as the RWD expert, collaborating with cross-functional teams.
• Contribute to defining the strategic direction of Freenome's data initiatives.
• Support data acquisition and lead data product implementation efforts.
• Oversee the translation of raw RWD datasets into research-ready, regulatory-grade datasets.
• Foster a quality mindset through data quality checks, governance policies, and SOPs.
• Develop the team, building technical and leadership capabilities.
• Directly lead a team of clinical informaticists and data analysts.
• Manage the RWD department budget.
• Build and maintain a culture of collaboration and inclusivity.

Requirements:

• Master's or Ph.D. in Biostatistics, Epidemiology, Health Informatics, Computer Science, or a related quantitative field.
• At least 12 years of progressive experience in clinical informatics.
• Experience with EHR, claims, and other structured and unstructured RWD, and molecular data.
• Experience applying RWD to device, therapeutic, and/or software product development.
• Experience with RWD across multiple disease areas, including oncology.
• At least 10 years of experience building and leading high-performing, multi-disciplinary teams.
• Demonstrated success operating within a matrixed organization.
• Excellent written and verbal communicator with strong stakeholder management capabilities and problem-solving skills.
• Experience leading data modeling, terminology normalization, data curation, and/or unstructured data abstraction via NLP.
• Deep knowledge of ontologies commonly used by health systems and payers.
• Experience with Python, R, and/or SQL.

Nice to haves:

• Experience supporting real-world evidence FDA submissions or payer/guideline dossiers.
• Clinical background and/or degree in health informatics.
• Experience with cloud-based data storage and processing environments.
• Experience working directly with health systems or data vendors on data licensing and ingestion projects.

Benefits:

• The US target range of our base salary rate for new hires is $236,300 - $335,475.
• Eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits.

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This role focuses on driving strategic improvements in data infrastructure, pipeline design, data quality, and analytics at scale. The ideal candidate will design scalable data solutions, automate complex workflows, and transform structured and unstructured data into actionable insights.

Key responsibilities:

• Develop a deep understanding of ZoomInfo's data assets, infrastructure, and data operations to identify opportunities for improving data quality, accuracy, coverage, and scalability.
• Design, build, and optimize scalable cloud-native data pipelines and automation solutions using SQL(Snowflake), BigQuery, Python, Databricks, PySpark, Spark, and ETL/ELT frameworks.
• Leverage AI/GenAI/LLMs (including Claude Code), AI integrations, MCPs, APIs, and web scraping technologies to automate data acquisition, enrichment, validation, and processing.
• Analyze large structured and unstructured datasets, design experiments, validate hypotheses, and deliver actionable insights through dashboards, KPIs, and reporting frameworks.
• Partner closely with Data Engineering, Product Management, and Research teams to drive strategic data initiatives from discovery through implementation.
• Lead end-to-end delivery of large-scale, cross-functional data projects while maintaining rigorous data quality standards across pipelines and deliverables.
• Translate complex technical analyses into clear, business-focused recommendations for both technical and non-technical stakeholders.
• Mentor and coach junior analysts, contribute to hiring and foster a culture of continuous learning, technical excellence, and collaboration.

Requirements:

• 7+ years of experience in Data Analytics / Data Operations / Data Engineering or Science, or a related field, with experience working in product- or project-driven environments.
• Strong expertise with hands-on experience in SQL, Snowflake, BigQuery, Python, PySpark, Apache Spark, Databricks, AWS, GCP, ETL/ELT pipeline development, REST APIs, and Excel, with proven experience handling large-scale structured and unstructured datasets.
• Hands-on experience with AI/GenAI/LLMs, including machine learning, NLP, prompt engineering, Claude Code, AI integration into Python applications, MCP (Model Context Protocol) integrations, and building AI-driven automation solutions.
• Strong experience in web scraping and data acquisition using tools such as Scrapy, Selenium, BeautifulSoup, and Playwright, along with a solid understanding of data quality, validation, enrichment, and governance.
• Excellent analytical, problem-solving, and communication skills, with the ability to simplify complex analyses into actionable insights and effectively present findings to both technical and non-technical stakeholders.
• Proven ability to manage multiple concurrent projects, lead cross-functional initiatives, mentor junior analysts, and work independently with a high degree of ownership and accountability.
• Experience building dashboards and reporting frameworks using visualization tools such as Tableau, Looker, or Power BI.

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You will build and maintain services, REST APIs, databases, and big data jobs that provide Backstory’s insights.

Working with the larger Engineering and Product teams, you will take 'activity' and 'CRM objects' data provided by other teams and make it available for consumption to end users, building higher-level customisable insights that power not only our web application, but also use cases like Account 360, Opp 360, search functionality, and our new Generative AI features.

Responsibilities

• Design, build, and maintain backend services, REST APIs, databases, and big data pipelines that power customer-facing insights and analytics.
• Implement and maintain near-real-time stream-based data processing pipelines in collaboration with batch-oriented data refresh workflows.
• Develop and evolve a query engine capable of answering complex, cross-deal/account questions and delivering actionable insights for sales managers and executives.
• Scale data processing and insights generation pipelines to handle growing volumes of activity data (emails, meetings, transcripts, CRM objects) while managing infrastructure costs.
• Collaborate with Engineering and Product teams to translate business and customer needs into robust, well-documented technical solutions.
• Follow and promote software development best practices, delivering clean, maintainable, and well-monitored code.
• Build internal tooling to enable customer support teams to investigate and resolve support requests in a self-service manner.
• Ensure high-quality alerting, dashboards, tracing, and runbooks are in place for all production services.

Requirements

• 5+ years of professional experience building backend systems in an enterprise environment
• 2+ years of experience programming in Python or Scala
• Experience with distributed systems and service-oriented architecture (SOA)
• Experience with large-scale data processing frameworks (Spark, Hive, Hadoop, MapReduce, or similar)
• Experience with stream processing technologies such as Kafka, Apache Samza, or Apache Storm
• Experience with Elasticsearch is a plus
• Experience with LLMs or NLP is a plus
• Familiarity with analytical databases and ETL pipeline design
• Strong analytical and problem-solving skills with a track record of delivering elegant, practical solutions
• Experience taking ownership in an unfamiliar or legacy codebase and incrementally improving it
• Strong inclination toward automation and reducing manual operational toil

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At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits.

The CX Intelligence team is part of Coinbase’s Enterprise Applications and Architecture org and builds the customer-facing and internal CX experiences that connect the Help Center, chatbots, and agent workflows. We are hiring an IC4 Machine Learning Engineer to help evolve our conversational ecosystem by building a seamless hybrid vendor-internal chatbot experience.

You will contribute to the design and implementation of a unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants.

Responsibilities:

• Build and improve the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
• Develop production-grade Python services that bridge advanced AI and ML capabilities with reliable customer-facing products.
• Drive well-scoped ML projects from design through delivery, balancing technical trade-offs and collaborating across teams.
• Contribute to system design, coding standards, and AI/ML development best practices across the team.
• Partner with engineers and cross-functional stakeholders to build secure, scalable, and high-performing AI-enabled experiences.
• Participate in design reviews and help ensure features meet Coinbase standards for security, compliance, and performance.

Requirements:

• 2+ years of professional experience in machine learning and software engineering, with experience shipping production-grade ML services.
• Hands-on experience building with modern AI architectures such as LLMs and deep learning, and familiarity with tools such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
• Strong proficiency in Python and the ability to write clean, maintainable, well-tested production code.
• Working knowledge in at least one domain such as NLP, information retrieval, computer vision, or statistical modeling.
• Ability to write technical design documents and communicate ML system designs clearly to cross-functional stakeholders.
• Uses generative AI responsibly, applying human oversight to deliver business-ready outputs and measurable improvements in efficiency, cost, and quality.

Benefits:

• The target annual base salary for this position can range from ₹4,408,400-₹4,408,400 INR.
• Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, and vision).

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The Ads Foundational Representations (AFR) team develops signals and representations of Reddit's core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs.

As a Senior ML Engineer, you'll be in charge of the full-cycle execution of ML projects - from collaborating with cross-functional teams on requirements and design, to the implementation of the feature and its experimentation.

Responsibilities:

• Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
• Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings.
• Building data processing and inference pipelines for the models we develop.
• Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments.
• Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management.
• Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance.
• Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.

Required Qualifications:

• 5+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
• Experience building NLP or CV models and integrating them at scale.
• Experience developing complex features/embeddings for downstream models.
• Experience with mainstream DL frameworks: PyTorch or TensorFlow.
• Excitement about working with data and readiness to look behind the metric numbers.

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Private Pension plan with Employer-matching
• 100% employer-sponsored group medical plan
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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The Ads Foundational Representations (AFR) team develops signals and representations of Reddit's core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs.

As a Senior ML Engineer, you'll be in charge of the full-cycle execution of ML projects - from collaborating with cross-functional teams on requirements and design, to the implementation of the feature and its experimentation.

Responsibilities:

• Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
• Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings.
• Building data processing and inference pipelines for the models we develop.
• Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments.
• Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management.
• Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance.
• Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.

Required Qualifications:

• 5+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
• Experience building NLP or CV models and integrating them at scale.
• Experience developing complex features/embeddings for downstream models.
• Experience with mainstream DL frameworks: PyTorch or TensorFlow.
• Excitement about working with data and readiness to look behind the metric numbers.

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Group Personal Pension Scheme with Employer match
• Private Medical and Dental Scheme
• Income Replacement Programs
• Bike to Work scheme
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

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As a Machine Learning Engineer on the Conversation Intelligence team, you'll develop and deploy solutions that extract meaning from voice and messaging data at Twilio scale. You'll work alongside experienced ML practitioners to ship real features - from model pipelines to production inference - that directly shape how businesses understand their customers.

Responsibilities:

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
• Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions.
• Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs.
• Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy.
• Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
• 2+ years of experience in machine learning engineering or applied ML, with demonstrated proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX) and familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy.
• Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment, including model versioning, experiment tracking, and cloud-based infrastructure (AWS, GCP, or Azure).
• Proficiency in Python (preferred) or similar OO language.
• Experience utilizing Large (or Small) Language Models within software systems.
• Excellent written and verbal communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.

Desired:

• Hands-on experience with conversational AI, or LLM fine-tuning and prompt engineering in a production context.
• Exposure to agentic AI frameworks such as LangGraph, AutoGen, CrewAI.
• Familiarity with MLOps/LLMOps tooling related to maintaining models in production such as testing, versioning, model registry, retraining, and monitoring.

Location - This role will be remote from Spain. Travel may be required occasionally to participate in project or team in-person meetings.

What We Offer: Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more.

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As a Machine Learning Engineer on the Conversation Intelligence team, you'll develop and deploy solutions that extract meaning from voice and messaging data at Twilio scale. You'll work alongside experienced ML practitioners to ship real features that directly shape how businesses understand their customers.

Responsibilities:

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
• Implement and maintain end-to-end AI/ML pipelines from data ingestion and feature engineering through to model development, validation, and deployment.
• Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health.
• Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies.
• Collaborate across planning, design, and code review phases contributing to product and technical discussions.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
• 2+ years of experience in machine learning engineering or applied ML, with demonstrated proficiency in Python and at least one ML framework.
• Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment.
• Proficiency in Python or similar OO language.
• Excellent written and verbal communication skills.

Desired:

• Hands-on experience with conversational AI, or LLM fine-tuning and prompt engineering in a production context.
• Exposure to agentic AI frameworks.
• Familiarity with MLOps/LLMOps tooling.

Location: This role will be remote from Ireland.

What We Offer: Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more.

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The Machine Learning team at Intercom is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in customers' hands.

Responsibilities:

• Identify areas where ML can create value for customers
• Identify the right ML framing of product problems
• Work with teammates and Product and Design stakeholders
• Conduct exploratory data analysis and research
• Deeply understand the problem area
• Research and identify the right algorithms and tools
• Be pragmatic, but innovate right to the cutting-edge when needed
• Perform offline evaluation to gather evidence an algorithm will work
• Work with engineers to bring prototypes to production
• Plan, measure & socialize learnings to inform iteration
• Partner deeply with the rest of team, and others, to build excellent ML products

Required skills:

• Broad applied machine learning knowledge
• 3-5 years applied ML experience
• Practical stats knowledge (experiment design, dealing with confounding etc)
• Intermediate programming skills
• Strong communication skills, both within engineering teams and across disciplines.
• Comfort with ambiguity
• Typically have advanced education in ML or related field (e.g. MSc)
• Scientific thinking skills

Bonus skills & attributes:

• Track record shipping ML products
• PhD or other experience in a research environment
• Deep experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering
• Strong stats or math background
• Visualization, data skills, SQL, matplotlib, etc.

Benefits:

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you're cycling, we've got you covered on the Cycle-to-Work Scheme. With secure bike storage too
• MacBooks are our standard, but we're happy to get you whatever equipment helps you get your job done
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated
• Relocation support for those moving to our offices

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The Machine Learning team at Intercom is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in customers' hands.

Responsibilities:

• Identify areas where ML can create value for customers
• Identify the right ML framing of product problems
• Work with teammates and Product and Design stakeholders
• Conduct exploratory data analysis and research
• Deeply understand the problem area
• Research and identify the right algorithms and tools
• Be pragmatic, but innovate right to the cutting-edge when needed
• Perform offline evaluation to gather evidence an algorithm will work
• Work with engineers to bring prototypes to production
• Plan, measure & socialize learnings to inform iteration
• Partner deeply with the rest of the team, and others, to build excellent ML products

Required skills:

• Broad applied machine learning knowledge
• 3-5 years applied ML experience
• Practical stats knowledge (experiment design, dealing with confounding etc)
• Intermediate programming skills
• Strong communication skills, both within engineering teams and across disciplines
• Comfort with ambiguity
• Typically have advanced education in ML or related field (e.g. MSc)
• Scientific thinking skills

Bonus skills & attributes:

• Track record shipping ML products
• PhD or other experience in a research environment
• Deep experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering
• Strong stats or math background
• Visualization, data skills, SQL, matplotlib, etc.

Benefits:

• Competitive salary and equity in a fast-growing start-up
• Lunch served every weekday, plus snacks and a fully stocked kitchen
• Regular compensation reviews
• Pension scheme & match up to 4%
• Life assurance, comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers
• Cycle-to-Work Scheme
• MacBooks provided
• Relocation support

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Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Fin can also be combined with our Helpdesk to become a complete solution called the Intercom Customer Service Suite, which provides AI enhanced support for the more complex or high touch queries that require a human agent.

Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service. Driven by our core values, we push boundaries, build with speed and intensity, and consistently deliver incredible value to our customers.

What's the opportunity?

Intercom’s AI Group is responsible for defining new ML products, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.

We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta in weeks after a successful offline test.

We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, novel applications of transformer neural networks and fine-tuned LLMs. We test and measure the real customer impact of everything we deploy.

We plan to double in size, and are on the lookout for an experienced manager to manage a team of highly-performing ML scientists. Engineering managers in Intercom are not just people managers: they also have a track record of IC contributions, and can stay close to the work of their team. We won’t ask you to write code every day, but you should feel comfortable giving technical advice to your team and having a good deep grasp of what it should (or shouldn’t!) work on (and maybe even do an occasional code review or a PR).

What will I be doing?

• Managing, mentoring and coaching a team of 10+ ML scientists, spread across our three offices in Dublin, London and Berlin.
• Collaborating with ML, engineering, product, and design leaders to shape work of the team and the impact it has on our products.
• Enabling your team to achieve high levels of productivity, performance and reliability.
• Expanding the team and promoting its brand both internally and externally (recruitment, event planning, etc.).

What skills might I need?

• Experience leading, developing and supporting ML teams that deliver impact quickly.
• A 5+ year track record of your own individual contributions in ML, AI, NLP, or data-related field.
• Excellent applied ML and coding skills.
• The ability to thrive in an ambiguous environment with a high degree of autonomy.
• Strong communication skills, both within technical teams and across disciplines.
• MSc or PhD in an ML-related field, or similar knowledge.

Bonus skills & attributes

• Track record shipping ML user-facing products.
• Experience in a research environment.
• Deep experience in an applicable ML area - e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering.
• Data skills: visualization, SQL etc.
• Deep knowledge of software engineering practices.
• Experience in recruiting ML scientists in a capacity similar to a hiring manager.

Benefits

• Competitive salary and equity in a fast-growing start-up.
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen.
• Regular compensation reviews - we reward great work!
• Pension scheme & match up to 4%.
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents.
• Flexible paid time off policy.
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones.
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too.
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

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As a Machine Learning Engineer on the CX Intelligence team within Enterprise Applications and Architecture, you'll build the AI-powered conversational systems that connect Coinbase's Help Center, chatbots, and agent workflows. The team owns the multi-agent platform powering Coinbase Chat and agent tooling, partnering with Conversation Design, CX, and Engineering to deliver secure, scalable automated support. You'll lead the design and implementation of a unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants, directly improving how millions of customers get help.

Responsibilities:

• Architect and deploy the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
• Build production-grade Python services that bridge advanced ML/AI research with reliable, measurable customer-facing products.
• Lead end-to-end project execution for complex ML initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through delivery.
• Establish best practices for system design, coding standards, and AI/ML development workflows across the team.
• Mentor engineers on architectural integrity and modern AI/ML patterns, raising the technical bar for the broader team.
• Conduct design reviews to ensure every feature meets Coinbase's standards for security, scalability, and performance.

Requirements:

• 5+ years of professional experience in machine learning and software engineering, with a track record of shipping production-grade ML services at scale.
• Hands-on expertise building with modern AI architectures (LLMs, deep learning) and the generative AI ecosystem, including frameworks such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
• Deep proficiency in Python with demonstrated ability to write clean, maintainable, highly-tested production code.
• Specialized knowledge in at least one domain: NLP, information retrieval, computer vision, or advanced statistical modeling.
• Proven ability to write technical design documents and present ML system architectures to cross-functional stakeholders, translating complex technical concepts for non-technical audiences.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Pay Transparency Notice: The target annual base salary for this position can range as detailed below. Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, and vision). Annual base salary range (excluding equity and bonus): R$455,500 - R$455,500 BRL

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The future of work is here, and it's at Cresta. Join us on this thrilling journey to revolutionize the workforce with AI.

At Cresta, we are dedicated to building state-of-the-art Machine Learning systems that power real-time, intelligent customer interactions. Our team develops models and platforms that process large-scale, multimodal data,especially speech and text,to extract meaning, improve quality, and deliver actionable insights at scale.

As a Senior Machine Learning Engineer, you will be at the forefront of applying modern ML and speech/NLP techniques to production systems. Your work will focus on improving ASR quality, building scalable evaluation and benchmarking infrastructure, and enabling continuous model iteration through data-driven insights.

Responsibilities

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems.
• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition, and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

Qualifications We Value

• Master’s or Ph.D. in Computer Science, Machine Learning, AI, or a related field.
• 5+ years of hands-on experience building, evaluating, and deploying ML models in production.
• Strong background in speech recognition (ASR), speech processing, or closely related domains.
• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

Perks & Benefits

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

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

Compensation at Cresta

Cresta’s approach to compensation is simple: recognize impact, reward excellence, and invest in our people. We offer competitive, location-based pay that reflects the market and what each individual brings to the table.

The posted base salary range represents what we expect to pay for this role in a given location. Final offers are shaped by factors like experience, skills, education, and geography. In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family.

Salary Range: $205,000–$270,000 + Offers Equity

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Machine Learning Engineers at Cresta work across several high-impact AI initiatives. Final team placement is determined based on experience, strengths, and business needs.

Current focus areas include:

• Agentic Assist: Lead and build next-generation agentic AI systems that augment contact center agents in real time.
• Agent & System Quality: Design evaluation frameworks and improve the reliability, robustness, and performance of LLM-powered agents.
• Insights: Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data.

Responsibilities:

• Lead the design and development of Cresta’s next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches.
• Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.
• Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models.
• Improve reasoning, planning, and tool-use capabilities in real-world AI applications.
• Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies.
• Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns.
• Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance.
• Optimize AI systems for scalability, latency, security, and cost efficiency in production environments.
• Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta’s platform.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta’s AI systems.

Qualifications We Value:

• Bachelor’s degree in Computer Science, Mathematics, or a related field; Master’s or Ph.D. preferred.
• 5–8+ years of industry experience building and deploying machine learning systems in production, including significant experience working with LLMs.
• Strong expertise in NLP, Generative AI, transformer architectures, embeddings, and retrieval systems.
• Proven experience designing and deploying Retrieval-Augmented Generation (RAG) systems in enterprise environments.
• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong knowledge of modern ML frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure.
• Demonstrated ability to optimize real-time ML systems for performance, scalability, and reliability.
• Strong technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Perks & Benefits:

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

Compensation at Cresta: Cresta’s approach to compensation is simple: recognize impact, reward excellence, and invest in our people. We offer competitive, location-based pay that reflects the market and what each individual brings to the table.

The posted base salary range represents what we expect to pay for this role in a given location. Final offers are shaped by factors like experience, skills, education, and geography. In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family.

Salary Range: $205,000–$270,000 + Offers Equity

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This internship offers hands-on exposure to modern AI stacks involving LLMs, retrieval-augmented generation (RAG), agent orchestration, and model adaptation, applied to real-world gaming and platform use cases.

You will contribute to building and improving autonomous and semi-autonomous AI agents, gaining experience across the AI development lifecycle,from data preparation and experimentation to system integration and evaluation,while learning best practices in scalable, production-oriented AI engineering.

**Key Responsibilities:"

Assist in designing and implementing agentic AI components, such as planning logic, tool usage, memory, and multi-step reasoning

Support the development and optimization of Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases

Help prepare datasets and experiment with LLM fine-tuning or adaptation techniques (e.g., instruction tuning, LoRA)

Contribute to internal tooling, frameworks, or workflows for LLM-driven agents

Integrate and experiment with 3rd-party AI services (LLMs, speech, vision, or agent frameworks)

Participate in benchmarking and evaluation of models, prompts, agent behaviors, and retrieval strategies

Assist with testing, debugging, and improving system performance, latency, and reliability

Collaborate with AI Engineers, Platform Engineers, and DevOps teams while learning industry best practices

Stay up to date with emerging trends in agentic AI, LLMs, and RAG systems

**Technical Skills:"

Students pursuing a degree in Computer Science, AI, ML, or a related technical field are welcome to apply

Strong programming skills in Python and familiarity with software engineering fundamentals

Basic understanding of large language models (LLMs) and prompt engineering concepts

Familiarity with at least one LLM API or open-source LLM (e.g., OpenAI, Claude, Gemini, LLaMA)

Exposure to or interest in RAG pipelines, agent frameworks, or ML workflows

Willingness to learn and experiment with modern AI tools and frameworks (e.g., LangChain, LlamaIndex)

Coursework, projects, or research related to NLP, LLMs, or agent-based systems

Experience from academic, personal, or hackathon projects involving: Prompt engineering, Vector databases or embeddings, Fine-tuning or adapting ML models

**Pre-Requisites:"

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To be successful in this role, you will need 5-8 years of applied ML experience, a previous background in a senior/staff role (data science, software development, or academic), significant demonstrated impact that your work has had on the product and/or the teams, strong programming skills, experience as the primary technical leader for a team, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically advanced education in ML or related field (e.g., MSc), and scientific thinking skills.

As a bonus, a track record of shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area (e.g., NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering), strong stats or math background, visualization, data skills, SQL, matplotlib, etc., would be beneficial.

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Key responsibilities include: Managing, mentoring, and coaching a team of 10+ ML scientists Collaborating with ML, engineering, product, and design leaders to shape the work of the team and its impact on our products Enabling your team to achieve high levels of productivity, performance, and reliability Expanding the team and promoting its brand both internally and externally

Requirements include: Experience leading, developing, and supporting ML teams that deliver impact quickly A 5+ year track record of individual contributions in ML, AI, NLP, or data-related fields Excellent applied ML and coding skills The ability to thrive in an ambiguous environment with a high degree of autonomy Strong communication skills, both within technical teams and across disciplines MSc or PhD in an ML-related field, or similar knowledge

Bonus skills and attributes include: Track record shipping ML user-facing products Experience in a research environment Deep experience in an applicable ML area, such as NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering Data skills, including visualization, SQL, and deep knowledge of software engineering practices Experience in recruiting ML scientists in a capacity similar to a hiring manager

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You will be expected to have 5-8 years of applied ML experience, a previous background in a senior/staff role (data science, software development, or academic), significant demonstrated impact that your work has had on the product and/or the teams, strong programming skills, experience as the primary technical leader for a team, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g., MSc), and scientific thinking skills.

As a bonus, you may have a track record of shipping ML products, a PhD or other experience in a research environment, deep experience in an applicable ML area (e.g., NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering), strong stats or math background, visualization, data skills, SQL, matplotlib, etc.

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The Role --------

We are looking for highly skilled Data Scientists to join our team. As a Data Scientist, you’ll design and deliver GenAI solutions (LLM/RAG) and applied ML components, taking prototypes through to production with strong evaluation, observability and governance. You will work closely with cross-functional teams, including data engineers, analysts, and business stakeholders, to turn data into actionable strategies that drive business outcomes.

Key Responsibilities --------------------

• Design and deliver GenAI solutions including LLM/RAG (retrieval strategy, embeddings, vector stores, prompt flows, grounding) for enterprise use cases.
• Evaluate and improve solution quality using offline/online metrics (quality, latency, cost) and iterate based on feedback.
• Harden solutions for production with observability/monitoring, tracing, guardrails, safety controls, and reliability practices
• Build and integrate model endpoints into products and workflows (APIs/services), partnering with engineering through to deployment.
• Work across cloud platforms (Azure/AWS/GCP) integrating storage, compute, orchestration, and model/runtime components.
• Assess data readiness for modelling/RAG (fitness, quality, access) and define remediation requirements
• Collaborate in cross-functional squads (DS/DE/engineering/product) and contribute to reusable assets and ways of working.
• Communicate clearly with stakeholders on trade-offs, evaluation results, risks, and adoption actions.
• Own end-to-end workstream delivery, lead stakeholder conversations, mentor others. (more senior levels)
• Shape solution direction and quality bar, coach teams, contribute to sales pursuits/bids and accelerators (most senior levels)

Requirements ------------

Essential Skills:

• Strong Python/R (pandas/NumPy; ML libs such as scikit-learn; DL frameworks TensorFlow/PyTorch).
• Experience with LLM/RAG toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search).
• Experience with GenAI platforms (e.g., OpenAI API, Anthropic, Gemini, Llama or equivalents).
• Exposure to big data/distributed computing and pipeline/feature engineering.
• LLM safety & governance (hallucination mitigation, grounded responses, audit trails)
• Degree in a quantitative field
• Right to work in the UK without sponsorship

Preferred Skills:

• Cloud ML experience (AWS/GCP/Azure).
• Strong SQL; experience with visualisation tools (Tableau/Power BI or Python viz).
• Specialisms: NLP / computer vision / time series.
• NoSQL familiarity.
• Quant / trading analytics engineering practices
• Time-series forecasting (prices, demand, blend outcomes, scheduling effects)
• Optimisation / simulation (planning, blending, logistics constraints)
• Model risk controls (bias/leakage checks, backtesting discipline, monitoring/drift)
• CI/CD, deployment, monitoring; Docker/Kubernetes.
• Experiment design and randomised trials.
• MSc with PhD a plus

Personal attributes

• Analytical, pragmatic problem-solver; outcome-oriented.
• Self-directed, able to prioritise and juggle multiple workstreams.
• Clear communicator who can simplify complexity.
• Collaborative, curious, continuous learner.

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Key responsibilities include:

• Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation
• Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience
• Contribute to building a robust framework for evaluating search ranking improvements - both offline and online

The ideal candidate will have a strong understanding of computer science fundamentals, experience applying LLM to search relevance, and contributions to well-used open-source projects.

At Databricks, you'll have the opportunity to shape the future of AI-driven products, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.

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Unlike traditional QA, this work sits at the intersection of AI behavior analysis, intent recognition evaluation, conversation quality assessment, model output auditing, and light prompt testing and refinement.

As an AI Quality Assurance Intern, you will:

• Execute structured test plans for AI Agent implementations
• Validate that intents, entities, and workflows trigger correctly
• Identify behavioral inconsistencies, misclassifications, or hallucinations
• Review and validate AI-generated suggestions, summaries, and classifications
• Provide actionable feedback to improve intent models and redaction/PII handling
• Run bias, redaction, and transcription accuracy checks
• Spot patterns or emerging issues across test sets
• Contribute ideas for improving internal QA processes and tools

This is an excellent fit for students interested in AI/ML, NLP, or chatbots, product quality and user experience, linguistics, conversation analysis, or human–AI interaction, or exploring a career in AI evaluation, AI ops, or product QA.

Perks & Benefits:

• $30-$50 per hour subject to taxes
• Lunch can be expensed (up to $25) while working in the office.
• PT0: 4 days

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We're looking for someone with 5-8 years of applied ML experience, previous background in a senior/staff role, and significant demonstrated impact that their work has had on the product and/or the teams. You should have strong programming skills, experience as the primary technical leader for a team, and strong communication skills, both within engineering teams and across disciplines.

As a bonus, we're looking for someone with a track record of shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area, and strong stats or math background.

We offer competitive salary and equity in a fast-growing start-up, regular compensation reviews, pension scheme, life assurance, comprehensive health and dental insurance, flexible paid time off policy, paid maternity leave, and relocation support for those moving to our offices.

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As a Machine Learning / AI Engineer, you will design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. You will also build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.

You will collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you will shape both foundational systems and user-facing capabilities.

Key responsibilities include:

• Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.
• Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
• Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.
• Work with product engineers to define and deliver impactful AI features across Figma's platform.
• Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.
• Explore new ideas at the edge of what's technically possible and help shape the long-term AI vision at Figma.

Requirements include:

• 5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI.
• Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring.
• Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost.
• Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality.
• Experience mentoring or leading others and contributing to a culture of technical excellence and innovation.

Preferred qualifications include:

• Familiarity with search relevance, ranking, NLP, or RAG systems.
• Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows.
• Experience working on creative or design-focused ML applications.
• Knowledge of additional languages such as C++ or Go is a plus, but not required.
• A product mindset with the ability to tie technical work to user outcomes and business impact.
• Strong collaboration and communication skills, especially when working across functions (engineering, product, research).

At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

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Key responsibilities include:

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

Requirements include:

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

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As a Research Engineer on Alignment Science, you'll collaborate with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team. Your responsibilities will include testing the robustness of our safety techniques, running multi-agent reinforcement learning experiments, building tooling to efficiently evaluate the effectiveness of novel LLM-generated jailbreaks, and contributing ideas, figures, and writing to research papers, blog posts, and talks.

You may be a good fit if you have significant software, ML, or research engineering experience, have some experience contributing to empirical AI research projects, and have some familiarity with technical AI safety research. Strong candidates may also have experience authoring research papers in machine learning, NLP, or AI safety, have experience with LLMs, have experience with reinforcement learning, and have experience with Kubernetes clusters and complex shared codebases.

The annual compensation range for this role is $350,000-$500,000 USD.

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At Cresta, we are on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioural best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster.

As a Senior Machine Learning Engineer, you will lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches. You will architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.

Responsibilities:

• Lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches.
• Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.
• Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models.
• Improve reasoning, planning, and tool-use capabilities in real-world AI applications.
• Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies.
• Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns.
• Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance.
• Optimize AI systems for scalability, latency, security, and cost efficiency in production environments.
• Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta's platform.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta's AI systems.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, or a related field; Master's or Ph.D. preferred.
• 5–8+ years of industry experience building and deploying machine learning systems in production, including significant experience working with LLMs.
• Strong expertise in NLP, Generative AI, transformer architectures, embeddings, and retrieval systems.
• Proven experience designing and deploying Retrieval-Augmented Generation (RAG) systems in enterprise environments.
• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong knowledge of modern ML frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure.
• Demonstrated ability to optimize real-time ML systems for performance, scalability, and reliability.
• Strong technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Perks & Benefits:

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

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Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $230K – $385K • 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 for eligible employees and 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 and safe time (1 hour per 30 hours worked)

• 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.

About the Team

Our infrastructure team helps deliver OpenAI’s most capable models and products to the world by scaling infrastructure and turning demand into useful FLOPS. We collaborate across research, engineering, design, and business to turn cutting-edge AI advancements into impactful, real-world applications. Our team ensures the right compute is available—at the right time and place—to support some of the world’s most demanding workloads. We empower all of OpenAI’s products and research by scaling the infrastructure behind them. Our work makes it possible to launch new models and products reliably and at scale.

About the Role

As a Data Scientist on the Infra team, you will play a key role in shaping how we scale the infrastructure that powers OpenAI’s products and research. This is critical as we operate one of the largest and most advanced compute fleets in the world, supporting millions of users and businesses globally. We focus on aligning infrastructure measurement, planning, scaling, allocation, and efficiency to drive measurable impact across the company.

You should expect to guide the definition of foundational datasets for infrastructure resources, develop metrics that inform key decisions, build forecasting and optimization models, and establish source of truth dashboards and analyses that enable teams to understand and improve infra usage. Most importantly, you should expect to be a core partner to engineering, research, and product teams in shaping the infrastructure that powers everything OpenAI builds.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Build and maintain foundational datasets and metrics that reflect infrastructure usage, efficiency, and scaling.

• Develop forecasting and optimization models to support infra planning and resource allocation.

• Partner with engineering, research, and product teams to shape infrastructure strategy through data.

• Drive clarity with source-of-truth dashboards and analyses that guide infra decisions across OpenAI.

You might thrive in this role if you have:

• 5+ years of experience in a quantitative role navigating ambiguous environments, ideally in infrastructure, systems, or platform domains at a high-growth company or research org

• Experience defining and operationalizing metrics that reflect system performance, resource usage, or efficiency from the ground up

• A strong foundation in SQL and Python, and a track record of building models and analyses that drive technical and strategic decisions

• Excellent communication skills and the ability to partner effectively with engineers, researchers, and product stakeholders

• A strategic mindset that goes beyond statistical testing to surface actionable insights and long-term tradeoffs

You could be an especially great fit if you have:

• Proven track record of operating as a data partner in large scale backend systems

• Comfortable navigating fast-paced execution while also anchoring decisions in long-term impact

• Strong programming background, with ability to run simulations and prototype variants

• Experience in NLP, large language models, or generative AI

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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Location

San Francisco; Seattle

Employment Type

Full time

Department

Data Science

Compensation

• $230K – $385K • 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 for eligible employees and benefits.

Benefits

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

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

• 401(k) retirement plan with employer match

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

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

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

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

• Relocation support for eligible employees

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

About the Team

Our Applied team brings OpenAI technologies to consumers and businesses around the world. We collaborate across research, engineering, design and business functions to turn cutting-edge AI advancements into impactful real-world applications. Our team has been behind notable product launches (ChatGPT, API, Sora), creating tools that help developers write code, enable businesses to operate more efficiently, and empower individuals to learn and create. As AI capabilities rapidly evolve, we focus on ensuring that our products are safe, accessible, and beneficial to all.

About the Role

As a Data Scientist on the Applied Product team, you will contribute to a data-driven product development culture for consumer and enterprise products at OpenAI. This is critical as our products reach millions of users and businesses worldwide. We are focused on aligning both research and product development to drive measurable impact for these individuals and organizations alike.

You should expect to define our north-star metrics, design A/B tests, and establish source-of-truth dashboards that the entire company can use to answer their own product questions. Most importantly, you should expect to be a core member of the product development team.

This role is based in San Francisco, CA or Seattle, WA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Embed with the product development team as a trusted partner, uncovering new ways to improve the product and drive growth

• Define and interpret A/B tests that help answer critical questions about the impact of model and UX changes to our product

• Establish a data-driven product development culture by defining, tracking, and operationalizing feature-, product-, and company-level metrics

• Develop and socialize dashboards, reports, and other ways of enabling the team and company to answer product data questions in a self-serve way

You might thrive in this role if you have:

• 5+ years experience in a quantitative role navigating highly ambiguous environments, ideally as an early data scientist or product analyst at a hyper-growth product company or research org

• Proposed, designed, and run rigorous experiments with clear insights and product recommendations utilizing SQL and Python

• Defined, implemented, and operationalized new feature and product-level metrics from scratch

• Excellent communication skills with demonstrated ability to communicate with product managers, engineers, and executives alike

• Strategic insights beyond the paradigm of statistical significance testing

You could be an especially great fit if you have:

• Strong programming background, with ability to run simulations and prototype variants

• Experience validating quantitative insights with qualitative methods (e.g. surveys, UXR)

• Demonstrated prior experience in NLP, large language models, or generative AI

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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What you'll do

• Apply state-of-the-art ML and LLM techniques to solve problems spanning:
• Personalization (LLM memory, context summarization, retrieval and ranking);
• Query Understanding (intent modeling, rewriting, agentic decomposition);
• Content Discovery (feed ranking and surfacing)
• Rigorously evaluate LLM/ML models with both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact.
• Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement.
• Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements.
• Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle.

What you need

• 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products.
• Deep expertise in deep learning (PyTorch, TensorFlow, JAX), LLMs, information retrieval, content summarization, recommendation systems, NLP, and/or ranking.
• Strong software engineering skills (Python, production-quality codebases, collaborative development).
• In-depth experience with the full ML lifecycle: data analysis, feature engineering, iterative model development, rigorous evaluation, and ongoing monitoring/improvement.
• Proven collaborator and communicator; excels in high-velocity, cross-functional teams.
• Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI.
• BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).

Bonus Points For

• Experience with LLM prompt engineering, Retrieval-augmented generation (RAG) based systems.
• Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).
• Open-source or published contributions in ML, NLP, IR, or relevant research fields.

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