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Location: Remote, Global ABOUT SUPABASE Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. ABOUT THE ROLE We're looking for a Release Engineer (SRE) to join our Release Engineering team (part of EngOps) — a production-operations expert who brings an SRE mindset to how Supabase ships and runs, making deploys safe, observable, and recoverable at scale. Release Engineering's scope has grown well beyond build-and-ship: we increasingly own the operational reliability of the systems that deploy and run Supabase. In this role you'll treat our deployment pipelines, pre-production signal, and the control plane itself as production systems — with SLOs, error budgets, and on-call ownership — and you'll be the person teams lean on when reliability is on the line. This is not a "gatekeeper" role. You'll make the reliable path the easy path: standardising how we deploy, instrumenting what we ship, and ensuring that when something breaks, we detect it quickly and recover quickly. WHAT YOU'LL BE RESPONSIBLE FOR In this role, you'll: - Own the reliability of Supabase's deployment and release systems, and the control plane they run on, against clear SLOs and error budgets - Turn pre-production into a trustworthy signal — standardizing and instrumenting today's fragmented, ad-hoc deployment workflows - Drive disaster-recovery readiness, including making environments reproducibly deployable from scratch (untangling undocumented secrets, unclear configuration ownership, and circular service dependencies) - Build and operate health and SLO monitoring for critical user flows, using synthetic testing to catch regressions before customers do - Reduce mean-time-to-detect and mean-time-to-recover for deploy-related incidents — which account for a large share of our incident load - Participate in on-call, lead blameless postmortems, and turn findings into runbooks, alerting, and automation that remove toil - Improve deployment observability and auditability — a clear record of what shipped where, when, and by whom - Document operational procedures — break-glass paths, access models, and runbooks — so reliability knowledge isn't tribal RELIABILITY & OPERATIONS - Define and track SLAs, SLOs, error budgets, and DORA delivery metrics — with meaningful alerting over noise - Ensure deployments fail fast and safely when health checks degrade - Harden access and break-glass workflows (e.g. scoped self-service) so the right people can act in an incident without unsafe workarounds - Partner with product engineering and platform teams to align release practices with reliability and availability targets YOU MIGHT BE A GOOD FIT IF YOU - Have 5+ years in SRE, production operations, platform engineering, or release engineering - Have operated production systems at scale and carried on-call for them - Are fluent in SLAs, SLOs, error budgets, DORA metrics, and operational KPIs — and the observability tooling behind them (Prometheus, Grafana, Alertmanager, or similar) - Have led incident response with tooling like incident.io http://incident.io (or PagerDuty / Opsgenie), run blameless postmortems, and driven down MTTD/MTTR - Operate confidently on AWS (multiple accounts, IAM, VPC) in production - Are comfortable with infrastructure-as-code (Pulumi, Terraform) and Kubernetes - Script and automate to eliminate toil rather than absorb it - Communicate clearly with both infrastructure specialists and product engineers - Thrive in async, globally distributed teams - Are comfortable navigating ambiguity and iterating toward better systems over time WHAT WE OFFER - Fully Remote We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world. - ESOP Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together. - Tech Allowance Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work. - Health Benefits Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us. - Annual Off-Sites Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year. - Flexible Work We operate asynchronously and trust you to manage your own time. You know what needs to be done and when. - Professional Development Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth. ABOUT THE TEAM Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love. - ~400 team members - 60+ countries - 20+ languages spoken - Over $1B raised (including our $500M Series F) - 540,000+ community members We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.

Location: New York, New York; New York City Metropolitan Area; New Jersey; Connecticut; San Francisco Bay Area, California Workplace: remote About the role: At Provectus, you don’t prototype in a sandbox and hand a client a slide deck. You embed inside a bank, an insurer, or a health system, learn the work, and ship a Claude + AWS system into their real, regulated production — then you watch the business number move. We’re an Anthropic and AWS Partner with 150+ certified architects. That means the newest models, real budgets, and problems that matter — without the layers, politics, and years-to-impact of a hundred-thousand-person firm. 350+ builders, 100+ customers in production, founder-led since 2010, offices across North America, LATAM, and EMEA. Why Provectus Frontier access — Anthropic Preferred Partner: newest Claude models, real budgets, real problems Real tasks (not scope), real deployment. You go where the operating problem is and ship production code to real users, not slide decks that sit in a drive Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Embedded is not staff-augmented Autonomous. You own the work from framing through to production, visible to clients and founders.  You own the method; nobody hands you a ticket Your experience stays portable. Every engagement is a new problem, often in a new industry, so your skills compound instead of narrowing to one product. No legacy-maintenance grind, not a single stack Craft & growth — build beside senior FDEs on a Claude-certified team; a real platform (talks, OSS) to grow your name We give you a stage: Claude certification, conference talks, open-source contributions, published work under your own name What You’ll Do: Spend the first weeks of each engagement in the operator’s seat, learning the constraints from the inside, then rebuild the function from first principles alongside the client and a Forward Deployed Executive — and you are the one who builds it Be part of the customer’s team, inside their process; work on real tasks, not scoped deliverables, and own the method autonomously You won’t start from zero. Provectus builds industry Blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it Deliver across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare Help shape how leading enterprises adopt AI, from strategy through first deployment; help build the tooling and frameworks of a growing AI delivery practice What We Look For: Typically 7+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way Genuinely willing to spend time understanding someone else’s job before writing code; demonstrated ability to become conversant in an unfamiliar business function quickly Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack Cloud-native delivery on AWS (GCP/Azure a plus), including the operational reality of a system someone else inherits Pre-sales experience. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job Hands-on experience with Claude, Claude Code, and Cowork (skills, workflows, MCP, integrations, connectors, custom harnesses) Nice to have: Containers and orchestration (Kubernetes/ECS) and CI/CD in production IaC depth: AWS CDK, CloudFormation, Terraform Open-source contributions or public writing on applied AI What We Offer: Full-time employment On-site visits to clients’ facilities and our office in NYC 1/week Unlimited Vacation policy Generous health, vision, and dental insurance 401(K) matching plan Base salary $180k–$200k + bonuses; OTE up to $250k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, and abilities of the applicant, and alignment with market data No Visa sponsorship How We Hire: Recruitment interview. Tech interview with our FDE (30 min). A live engineering building session (1.5 hours). We hand you a business function and the constraints. You have to understand the job and rebuild it — then say what you’d build and how you’d know it worked. Use AI the way you normally would; we expect AI-generated code. What we look at is how you used AI, the judgment behind your choices, and what you researched beyond what any LLM already knows. 4. Soft skills interview. 5. Offer discussion. We’re real: A person reads your application. We filter out AI-generated and fraudulent submissions automatically. Every application that clears that filter is read by someone on our team We may ask for a quick ID verification. Fraudulent applications are common, and this protects real candidates You’ll meet the people you’d work with. We host AI engineering events in New York and San Francisco. We'd be happy to talk in person. Your interviews are with Pavel Borobov, Solutions Architect, and Stepan Pushkarev, Co-Founder & CEO

Location: Barcelona, Spain Workplace: remote Welcome to OLX  We're building marketplace sustainable ecosystems that millions of people depend on every month – to buy and sell cars, find homes, land jobs, and trade secondhand goods. It's commerce that matters: safe, smart, and environmentally responsible. We do this with courage to innovate, commitment to outcomes, collaboration across 50+ nationalities and 7 markets, and a customer-led approach that puts real people first. Add a desire to win – and you get a culture that's ambitious, fast-moving, and built on trust. Our brands – including AutoTrader, OLX, Otomoto, Property24, and more – are loved by millions. And we're backed by Prosus, one of the world's largest tech investors.  Join OLX and see how far you can go. We believe the future of our products sits at the intersection of AI and design. Our ambition is to become an AI-native organization, where intelligent systems and human-centered design come together to create faster, smarter, and more meaningful customer experiences. We’re rethinking how products are built, how experiences are shaped, and how teams deliver impact. A key part of this transformation is creating the right foundations and infrastructure to enable high-quality experiences at scale - where design systems, patterns, and workflows are structured, scalable, and AI-readable, unlocking new levels of speed, consistency, and innovation. The role We're looking for a Senior Product Designer to join our Design System team, working across our Real Estate products. You'll play a key role in evolving our design system, helping designers and engineers across the business design and build with greater consistency, quality and pace. You'll work day to day within a small, dedicated pod, spanning web and app. But the system serves designers across multiple tribes, each shipping their own product work. You'll work within our contribution model, helping teams outside the pod propose, adopt and evolve components without fragmenting the system. We're looking for someone with strong craft, attention to detail, and curiosity about systems thinking and AI-assisted workflows. You'll help create a coherent product experience across our brands while developing your own design systems practice. Key responsabilities: Design and evolve reusable components, patterns and foundations within our design system, reducing duplication and improving consistency across tribes Define component behaviour, states and variants, working directly with engineering to ship them Run and improve the contribution model: the process and standards that let designers outside the core pod propose and ship components without breaking system coherence Support design work from problem framing through to documentation and handover, using research and feedback from teams to inform decisions Create clear documentation and specifications covering variants, edge cases and usage guidance Collaborate closely with engineers on token strategy and the practical link between Figma and production code Resolve conflicts between a tribe's local need and the system's long-term coherence Work with product design teams to identify gaps and embed stronger systems thinking across products AI-enabled Design Systems: Use AI-assisted tooling as part of your daily workflow, from research through to prototyping and component definition Contribute to making the system well-structured for AI-assisted workflows, including tokens, documentation and reusable patterns Know when to override AI output with your own design judgment, and be able to explain why What we're looking for: Proven experience designing and maintaining a design system at scale, not just documenting one Experience running or contributing to a contribution model, or a clear point of view on how you'd build one Strong technical fluency: you understand how your decisions translate into code, and work with engineers as a peer, not a handoff Sharp judgment on trade-offs: when to enforce consistency, when to allow exceptions Strong communicator, able to justify design system decisions to stakeholders who don't share your priorities, and bring other designers along rather than dictate to them Comfortable with AI-assisted tools, with an interest in applying them in practical ways to improve speed, quality and consistency Required experience A portfolio demonstrating strong product and systems thinking, with evidence of shipped impact, not just documented patterns Experience working in Figma, including component libraries, variables and tokens, alongside experience with built or coded prototypes Experience working closely with engineers to deliver high-quality, scalable outcomes Experience or strong interest in AI-assisted design or development workflows Hiring Process First Stage - Recruiter Screen This is your first interview with our Talent Acquisition team. We’ll explore your background, skills, and experience, as well as your approach to autonomy, ownership, and critical thinking. It’s also a chance to discuss your aspirations and ensure alignment with our culture and values. Second Stage - Portfolio Review In this stage, you’ll meet with the Hiring Manager and a Designer from the team. We’ll explore your process thinking and approach to design through a case study of your choice. This is also a great opportunity to discuss how you collaborate with others and how your values align with our team culture. Third Stage  - AI Sandbox Challenge This is a dynamic and interactive session where you’ll have the chance to demonstrate your thought process in action. It’s designed to give us insight into how you approach challenges, integrate AI into your workflows, and apply your skills in a practical setting. We aim to make this process clear, collaborative, and reflective of the way we work as a team. DEI statement OLX values diversity and welcomes applicants of all races, ethnicities, disabilities, ages, genders, sexual orientations, religions, backgrounds, and experiences to apply. If this role excites you, we encourage you to apply, even if you don't meet all the requirements - you may be just the right fit for this or other opportunities with us.  Privacy Data disclaimer OLX will process your personal data to assess your fit for the applied position. You may optionally consent to store your data for up to 12 months after the application process for consideration in future OLX roles. For more details on how your application data will be processed please check our Privacy Statement.

Location: New York; Massachusetts; Washington DC-Baltimore Area; Pennsylvania Workplace: remote About the role: We’re an Anthropic and AWS Partner with 150+ certified architects. That means the newest models, real budgets, and problems that matter — without the layers, politics, and years-to-impact of a hundred-thousand-person firm. 350+ builders, 100+ customers in production, founder-led since 2010, offices across North America, LATAM, and EMEA. Why Provectus Frontier access — Anthropic Preferred Partner: newest Claude models, real budgets, real problems Real tasks (not scope), real deployment. You go where the operating problem is and ship production code to real users, not slide decks that sit in a drive Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Embedded is not staff-augmented Autonomous. You own the work from framing through to production, visible to clients and founders.  You own the method; nobody hands you a ticket Your experience stays portable. Every engagement is a new problem, often in a new industry, so your skills compound instead of narrowing to one product. No legacy-maintenance grind, not a single stack Craft & growth — build beside senior FDEs on a Claude-certified team; a real platform (talks, OSS) to grow your name We give you a stage: Claude certification, conference talks, open-source contributions, published work under your own name What You’ll Do: Spend the first weeks of each engagement in the operator’s seat, learning the constraints from the inside, then rebuild the function from first principles alongside the client and a Forward Deployed Executive — and you are the one who builds it Be part of the customer’s team, inside their process; work on real tasks, not scoped deliverables, and own the method autonomously You won’t start from zero. Provectus builds industry Blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it Deliver across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare Help shape how leading enterprises adopt AI, from strategy through first deployment; help build the tooling and frameworks of a growing AI delivery practice What We Look For: Typically 7+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way Genuinely willing to spend time understanding someone else’s job before writing code; demonstrated ability to become conversant in an unfamiliar business function quickly Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack Cloud-native delivery on AWS (GCP/Azure a plus), including the operational reality of a system someone else inherits Pre-sales experience. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job Hands-on experience with Claude, Claude Code, and Cowork (skills, workflows, MCP, integrations, connectors, custom harnesses) Nice to have: Containers and orchestration (Kubernetes/ECS) and CI/CD in production IaC depth: AWS CDK, CloudFormation, Terraform Open-source contributions or public writing on applied AI What We Offer: Full-time employment On-site visits to clients’ facilities and our office in NYC 1/week Unlimited Vacation policy Generous health, vision, and dental insurance 401(K) matching plan Base salary $180k–$200k + bonuses; OTE up to $250k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, and abilities of the applicant, and alignment with market data No Visa sponsorship How we hire: Recruitment interview. Tech interview with our FDE (30 min). A live engineering building session (1.5 hours). We hand you a business function and the constraints. You have to understand the job and rebuild it — then say what you’d build and how you’d know it worked. Use AI the way you normally would; we expect AI-generated code. What we look at is how you used AI, the judgment behind your choices, and what you researched beyond what any LLM already knows. 4. Soft skills interview. 5. Offer discussion. We’re real: A person reads your application. We filter out AI-generated and fraudulent submissions automatically. Every application that clears that filter is read by someone on our team We may ask for a quick ID verification. Fraudulent applications are common, and this protects real candidates You’ll meet the people you’d work with. We host AI engineering events in New York and San Francisco. We'd be happy to talk in person. Your interviews are with Pavel Borobov, Solutions Architect, and Stepan Pushkarev, Co-Founder & CEO

Location: Atlanta, United States; Raleigh, United States; Philadelphia, United States; Tampa, United States; Baltimore, United States Top 3 Reasons To Join Us Competitive Salary 100% Remote Working on the latest tech for the Insurtech Market Leader About Us At CoverGo, our mission is to empower all insurance companies to make insurance 100% digital and accessible to everyone. We are a leading global no-code insurance platform for health, life, and P&C We’re the winner of the Insurtech of the Year in all of Asia and other awards globally We work with insurance enterprise clients such as AXA, Bupa, MSIG, Dai-ichi, Bank of China Group Insurance, and many more We're an international, diverse team of over 120 people with 30 nationalities and team members working remotely from all over the world We are fully funded and backed by reputable VC funds and strategic institutional investors We have a global presence in Asia, EMEA and the Americas We’ve grown our annualized revenue by over 30x since January 2021 We’re constantly working towards making CoverGo a workplace that you love coming to. We deeply believe that bringing together a diversity of thoughts, expressions, and perspectives is key to building the best culture for equally diverse communities all over the world What You Will Do: Work with clients and technical resources to identify their business problems and develop the right approach and analytical solution Plan and design simple to semi-complex business processes and system modifications Make recommendations to improve and support business activities Gather business requirements through a variety of techniques such as work sessions and interviews then analyze and document client complex business requirements and processes; communicate these requirements to appropriate parties Create complex test case scenarios to be used in testing; monitor/oversee the testing of business applications to verify that all client requirements are incorporated into the system design Develop business requirements documents/user stories to support the required modifications May plan, allocate and monitor work of other business analysts Understand simple to complex systems and related data to surface actionable insights, demonstrate sound judgment and decision-making skills Assist in providing time estimates for project related tasks Aid in updating process and procedural documentation Develop basic understanding of technical development cycle of mobile application or web portals Support QA, UAT and Release phases of the project via defect analysis, change maintenance, etc. Any other task that may be assigned What We Need: Minimum 5 years of experience as Business Analyst A degree in Business, IT or any relevant field Previous experience working in the health insurance industry is required Insurtech experience is preferred Health Insurance-related domain certification from INS/LOMA or III is an added advantage Knowledge of business analysis concepts of defining requirements, translating to technical solutions, and Agile approach to prioritizing work to epics, sprints Business analysis skills including facilitation, process documentation, requirements gathering and user acceptance testing Good organizational/time management skills to prioritize work and meet deadlines within defined timeframes Experience in an Agile and/or Scrum environment is a must Basic knowledge in generating process documentation, and document system functionality, data integration, and workflow Excellent written and verbal communication skills in English Technical writing skills is a plus Why You'll Love Working Here Fully Remote Flexible Leave International Environment Competitive renumeration package Performance Bonus Company activities and events Learning and development plan Remote work allowance CoverGo Company Video By submitting your application, you confirm that you have read, understood, and accepted the content of CoverGo’s Privacy Notice and you consent to the processing of your data as part of this application.

Location: Mexico City, Mexico; Guadalajara, Mexico; Monterrey, Mexico Top 3 Reasons To Join Us Competitive Salary 100% Remote Working on the latest tech for the Insurtech Market Leader About Us At CoverGo, our mission is to empower all insurance companies to make insurance 100% digital and accessible to everyone. We are a leading global no-code insurance platform for health, life, and P&C We’re the winner of the Insurtech of the Year in all of Asia and other awards globally We work with insurance enterprise clients such as AXA, Bupa, MSIG, Dai-ichi, Bank of China Group Insurance, and many more We're an international, diverse team of over 120 people with 30 nationalities and team members working remotely from all over the world We are fully funded and backed by reputable VC funds and strategic institutional investors We have a global presence in Asia, EMEA and the Americas We’ve grown our annualized revenue by over 30x since January 2021 We’re constantly working towards making CoverGo a workplace that you love coming to. We deeply believe that bringing together a diversity of thoughts, expressions, and perspectives is key to building the best culture for equally diverse communities all over the world What You Will Do: Work with clients and technical resources to identify their business problems and develop the right approach and analytical solution Plan and design simple to semi-complex business processes and system modifications Make recommendations to improve and support business activities Gather business requirements through a variety of techniques such as work sessions and interviews then analyze and document client complex business requirements and processes; communicate these requirements to appropriate parties Create complex test case scenarios to be used in testing; monitor/oversee the testing of business applications to verify that all client requirements are incorporated into the system design Develop business requirements documents/user stories to support the required modifications May plan, allocate and monitor work of other business analysts Understand simple to complex systems and related data to surface actionable insights, demonstrate sound judgment and decision-making skills Assist in providing time estimates for project related tasks Aid in updating process and procedural documentation Develop basic understanding of technical development cycle of mobile application or web portals Support QA, UAT and Release phases of the project via defect analysis, change maintenance, etc. Any other task that may be assigned What We Need: Minimum 5 years of experience as Business Analyst A degree in Business, IT or any relevant field Previous experience working in the health insurance industry is required Insurtech experience is preferred Health Insurance-related domain certification from INS/LOMA or III is an added advantage Knowledge of business analysis concepts of defining requirements, translating to technical solutions, and Agile approach to prioritizing work to epics, sprints Business analysis skills including facilitation, process documentation, requirements gathering and user acceptance testing Good organizational/time management skills to prioritize work and meet deadlines within defined timeframes Experience in an Agile and/or Scrum environment is a must Basic knowledge in generating process documentation, and document system functionality, data integration, and workflow Excellent written and verbal communication skills in English Technical writing skills is a plus Why You'll Love Working Here Fully Remote Flexible Leave International Environment Competitive renumeration package Performance Bonus Company activities and events Learning and development plan Remote work allowance CoverGo Company Video By submitting your application, you confirm that you have read, understood, and accepted the content of CoverGo’s Privacy Notice and you consent to the processing of your data as part of this application.

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a VP of Research (Machine Learning) to define and lead the research direction behind its AI platform. This leadership role will shape how AI systems reason, learn, evaluate, and continuously improve within a product designed for high-frequency, real-world usage. Working closely with product and engineering leadership, this position will drive the long-term intelligence strategy, balancing cutting-edge research with practical production impact. Key Responsibilities Define and evolve the research roadmap for the platform's core AI intelligence, including context representation, memory, reasoning, planning, and orchestration. Evaluate and determine when to develop proprietary model architectures versus leveraging or adapting frontier open-source and commercial AI models. Design evaluation frameworks that measure real-world performance, robustness, safety, and long-term system behavior beyond traditional benchmark metrics. Lead the company's AI alignment, safety, and guardrail strategy as a core component of product development. Drive research and experimentation across advanced machine learning techniques, including: Retrieval-Augmented Training (RAG) Mixture of Experts (MoE) Model Distillation Multi-Agent Orchestration Multimodal AI Systems Partner closely with product and engineering teams to define and execute the intelligence strategy for AI-powered applications. Establish and maintain high standards for research quality, technical judgment, and engineering excellence across the organization. Requirements Extensive experience designing, building, and deploying machine learning systems in production environments. Strong technical expertise in model behavior, failure analysis, system evaluation, and long-term AI performance. Proven ability to translate advanced research into reliable, production-ready AI solutions. Demonstrated experience making high-impact technical decisions in fast-moving and ambiguous environments. Deep understanding of AI evaluation methodologies, model robustness, safety, and system reliability. Strong ownership mindset with the ability to lead technical strategy and execution. Passion for building practical AI systems that deliver measurable real-world impact rather than focusing solely on academic research or benchmark performance. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance. Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost. Key Responsibilities Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment. Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation . Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies. Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products. Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization. Requirements Proven experience building and deploying production-grade machine learning systems used by real users. Strong expertise working with large language models and understanding model behavior, limitations, and failure modes. Experience developing scalable ML infrastructure, training pipelines, and inference systems. Strong software engineering skills with the ability to write maintainable, production-quality code. Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety. Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment. Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Senior Machine Learning Engineer to design and build production-grade machine learning systems that power its AI platform. This is a hands-on technical role focused on solving complex machine learning challenges, translating research into scalable solutions, and delivering reliable AI systems that operate effectively in real-world environments. Working closely with research, product, and engineering teams, this position will independently own critical machine learning components from development through production deployment. Key Responsibilities Build and develop core machine learning systems that power AI-driven product capabilities. Own the end-to-end machine learning lifecycle, including data preparation, model training, evaluation, inference, deployment, and continuous iteration. Translate research concepts into reliable, production-ready machine learning solutions. Investigate and resolve model performance issues and production system failures using real-world operational insights. Continuously improve machine learning systems through rapid experimentation, measurement, and iteration. Collaborate closely with research, product, and engineering teams to deliver impactful AI features. Provide technical guidance through code reviews, best practices, and mentorship of fellow machine learning engineers. Develop solutions that balance real-world production requirements, including latency, scalability, cost efficiency, reliability, and safety. Requirements Proven experience building and deploying production machine learning systems used by real users. Strong understanding of modern machine learning models, including their behavior, limitations, and failure modes in production environments. Excellent software engineering skills with the ability to develop robust, maintainable, production-quality systems. Experience designing and implementing scalable machine learning pipelines and infrastructure. Strong ownership mindset with the ability to independently solve complex technical challenges from concept through deployment. Excellent problem-solving, communication, and collaboration skills within cross-functional engineering teams. Demonstrated ability to iterate quickly and continuously improve machine learning systems based on production feedback. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Principal Machine Learning Engineer to build and deploy production-grade machine learning systems that power its AI platform. This role focuses on translating research into scalable solutions by developing robust training pipelines, inference systems, evaluation frameworks, and deployment infrastructure. Working closely with research and application engineering teams, this position will play a key role in delivering reliable, high-performance ML systems that operate effectively under real-world production constraints. Key Responsibilities Build and own end-to-end machine learning pipelines covering data processing, model training, evaluation, inference, and deployment. Fine-tune and adapt models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation. Design and operate scalable inference systems while balancing latency, cost, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to assess model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU optimization, memory efficiency, latency reduction, and scaling strategies. Collaborate with application engineering teams to integrate machine learning systems into backend, mobile, and desktop applications. Continuously improve ML systems through rapid iteration and real-world performance monitoring while balancing production constraints such as latency, cost, reliability, and safety. Requirements Strong background in deep learning and transformer-based architectures. Hands-on experience training, fine-tuning, or deploying large-scale machine learning models in production. Proficiency with modern machine learning frameworks such as PyTorch or JAX . Experience with distributed training and inference frameworks, including technologies such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray . Strong software engineering skills with experience building robust, maintainable, production-grade systems. Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision. Ability to independently own end-to-end machine learning systems in fast-moving environments. Strong problem-solving skills with a focus on rapid iteration and continuous improvement. Preferred Qualifications Experience with one or more of the following is preferred: LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer Open-source contributions to machine learning or systems libraries Scientific computing, compiler technologies, or GPU kernel development Reinforcement Learning from Human Feedback (RLHF) pipelines, including PPO, DPO, or ORPO Training or deploying multimodal or diffusion models Large-scale data processing frameworks such as Apache Arrow, Spark, or Ray

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking an iOS Software Engineer to build and enhance its AI-native mobile experience. This role is responsible for developing production-grade iOS applications where AI interactions are central to the user experience, ensuring performance, reliability, and user trust across all mobile interactions. Working closely with backend and machine learning teams, this position will focus on delivering scalable iOS applications that integrate advanced AI capabilities while maintaining a seamless and intuitive user experience. Key Responsibilities Build and maintain production iOS applications using Swift and SwiftUI . Integrate AI-powered features through backend APIs. Design user experiences for AI interactions, including loading states, streaming responses, retries, and fallback mechanisms. Optimize application performance, memory usage, and battery efficiency. Implement user feedback and analytics mechanisms to support AI evaluation and continuous product improvement. Collaborate closely with backend and machine learning engineers on API design and system behavior. Maintain high standards of application quality, stability, and reliability for App Store releases. Requirements Minimum 3 years of iOS development experience using Swift. Hands-on experience integrating AI-powered features into mobile applications. Strong understanding of async/await , concurrency, and background task management. Solid experience optimizing iOS application performance, memory usage, and responsiveness. Experience developing, releasing, and maintaining production iOS applications. Familiarity with designing applications that operate reliably under latency, partial failures, and non-deterministic AI behavior. Exposure to CoreML or lightweight on-device machine learning is an advantage. Familiarity with feature flags or remote configuration systems is preferred. Strong understanding of user experience design for AI-powered applications, with an emphasis on usability and user trust. Preferred Technical Skills Experience with the following technologies is preferred: Swift SwiftUI Swift Package Manager (SPM) SQL and NoSQL databases TensorFlow Lite (on-device inference)

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is looking for a Full Stack Engineer – AI Systems to build AI-native product experiences that transform advanced AI capabilities into intuitive, production-ready workflows. This role combines frontend, backend, and AI engineering to develop intelligent features that are reliable, scalable, and user-centric. Working closely with product, backend, and machine learning teams, this position will focus on designing end-to-end AI workflows, integrating large language models (LLMs), and delivering seamless user experiences across web applications. Key Responsibilities Build end-to-end product features across frontend, backend, and AI integrations. Design and develop AI agent workflows that support planning, tool usage, failure handling, and recovery across multiple steps. Integrate LLMs, memory systems, and external tools into scalable, production-grade applications. Design responsive AI interactions with streaming capabilities, partial results, and low-latency performance. Improve system reliability, observability, and fallback mechanisms for AI-powered features. Collaborate closely with product, backend, and machine learning teams to deliver features from concept to production. Continuously iterate and improve product functionality based on user feedback, production insights, and real-world usage. Requirements Strong experience in full stack software engineering across frontend and backend development. Solid understanding of system design, scalable architecture, and API development. Experience building AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or similar technologies. Ability to work effectively in fast-paced environments with evolving requirements. Strong ownership mindset with experience delivering features from ideation through production deployment. Excellent problem-solving skills and the ability to make pragmatic engineering decisions in ambiguous situations. Preferred Technical Skills Experience with some or all of the following technologies is preferred: Next.js Python Node.js PyTorch OpenAI, Anthropic, or open-source Large Language Models (LLMs) SQL and NoSQL databases Kubernetes Docker

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking an Android Software Engineer to build and enhance its AI-native mobile experience. This role is responsible for developing production-grade Android applications where AI is at the core of the user experience, ensuring performance, reliability, and usability across all mobile interactions. Working closely with backend and machine learning teams, this position will focus on delivering scalable Android applications that integrate advanced AI capabilities while maintaining a seamless user experience. Key Responsibilities Build and maintain production Android applications using Kotlin . Integrate AI-powered features such as chat, vision, voice, and recommendation systems through backend APIs. Design intuitive user experiences for AI interactions, including streaming responses, retries, and partial results. Optimize application performance, memory usage, and responsiveness for AI-intensive workflows. Implement analytics, logging, and user feedback mechanisms to support continuous AI evaluation and product improvement. Collaborate closely with backend and machine learning engineers to define API integrations and system behavior. Ensure application stability, security, and scalability within production environments. Requirements Minimum 3 years of Android application development experience using Kotlin. Hands-on experience integrating AI-powered features, such as Large Language Models (LLMs), computer vision, or speech APIs. Strong understanding of asynchronous programming using Coroutines and Flow . Experience working with RESTful or gRPC APIs and structured data formats. Strong debugging, troubleshooting, and performance profiling skills. Experience developing applications that operate reliably under conditions involving latency, partial failures, and non-deterministic behavior. Familiarity with ML Kit or lightweight on-device inference is an advantage. Experience publishing and maintaining production applications on the Google Play Store . Preferred Technical Skills Experience with the following technologies is preferred: Kotlin / Java SQL and NoSQL databases TensorFlow Lite (on-device inference)

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a AI Backend Engineer to build and operate the backend infrastructure that powers AI-driven experiences across its products. The role focuses on owning the inference and orchestration layer that sits between AI models and end users, ensuring every interaction is fast, reliable, scalable, and cost-efficient. Working closely with engineering and AI teams, this position will play a key role in designing production-grade backend services, optimizing system performance, and delivering robust AI-powered features across mobile and desktop platforms. Key Responsibilities Build, maintain, and operate backend systems that power AI-enabled features in production. Design and implement AI inference pipelines, orchestration layers, and service architectures around AI models. Develop scalable, high-performance APIs that integrate seamlessly with frontend applications and machine learning systems. Optimize system performance by improving inference latency, throughput, caching, batching, and streaming capabilities. Own production operations, including monitoring, logging, alerting, and incident response. Troubleshoot and resolve issues within distributed systems to ensure service reliability and stability. Continuously improve backend architecture and production systems based on operational insights and real-world usage. Collaborate with cross-functional teams to deliver reliable, scalable, and high-quality AI-powered solutions. Requirements Strong backend engineering experience in production environments. Proven experience building and operating high-throughput, low-latency backend services. Experience designing scalable APIs and distributed systems. Familiarity with AI inference patterns, including Large Language Models (LLMs), embeddings, and multimodal AI systems. Strong debugging and troubleshooting skills within distributed production environments. Demonstrated ability to deliver production-ready solutions while iterating based on real-world system performance. Excellent analytical, problem-solving, and collaboration skills. Technical Skills Experience with the following technologies is preferred: Python Node.js PyTorch OpenAI, Anthropic, or open-source Large Language Models (LLMs) SQL and NoSQL databases Kubernetes Docker

Mayflower is a technology company focused on transforming the live entertainment industry by providing high-quality live streaming experiences. The Design System Designer role involves evolving and maintaining the design system across product and engineering teams. Responsibilities: — Evolve and maintain the existing UI foundation using Figma, Storybook, and code — Design and refine core UI components such as inputs, buttons, form elements, and patterns — Refine and extend a scalable token architecture (primitive → semantic → component) with theming support — Improve alignment between Figma, frontend implementation, and Storybook to ensure product consistency — Contribute to and improve the design-to-code workflow using Figma Variables, Tokens Studio, Style Dictionary, and CI/CD pipelines — Support migration of components toward a token-based structure, prioritizing impactful areas — Help shape and maintain contribution guidelines and system standards for design and engineering teams — Contribute to documentation and knowledge sharing to facilitate system understanding and usage — Explore and support AI-assisted workflows, including structured and machine-readable design system practices

Job Description Action1 is transforming endpoint security for modern IT teams. As we continue our evolution from SMB to enterprise, we are hiring a high-performing Account Executive to drive new business across state, local, and education accounts in the United States. This is a quota-carrying, full-cycle role focused on landing and expanding SLED accounts while applying the same disciplined, new-business sales motion we use across mid-market and enterprise organizations. You will engage directly with IT Directors, Security Leaders, Infrastructure teams, procurement stakeholders, and executive decision-makers to position Action1’s patch management and endpoint security platform as a strategic solution. We’re looking for someone who understands how cybersecurity and IT infrastructure buying works — longer sales cycles, multiple decision-makers, technical validation, security review, procurement navigation, and strong business justification — and who can bring that discipline into public sector and education accounts. This is not a relationship-only SLED role or a reactive RFP position. We need someone who can create pipeline, open doors with IT and security buyers, run strong discovery, manage technical evaluations, and close business in a high-growth, remote-first SaaS environment. If you’ve successfully sold cybersecurity or adjacent IT solutions into mid-market, enterprise, or public sector accounts and enjoy building a pipeline as much as closing, this role is for you. Responsibilities — Drive SLED Revenue Growth: Own a defined U.S. SLED territory and meet or exceed quota targets through disciplined pipeline generation, account planning, and deal execution. — Manage the Full Sales Cycle: Lead opportunities from prospecting through close — including discovery, stakeholder mapping, value alignment, technical validation, procurement navigation, negotiation, and contract execution. — Sell to IT & Security Leaders: Engage IT Managers, Directors of Infrastructure, CISOs, CIOs, SysAdmins, security teams, and technical buying committees with credibility and consultative expertise. — Build & Maintain Strong Pipeline Coverage: Generate pipeline through outbound prospecting, partner collaboration, and strategic account planning. Maintain disciplined forecasting and opportunity hygiene in Salesforce. — Navigate Complex Buying Processes: Manage multi-threaded sales cycles, security reviews, procurement workflows, public sector buying requirements, and competitive displacement strategies. — Partner with Solutions Engineers: Collaborate closely with pre-sales/technical resources to ensure strong technical validation and alignment with customer requirements. — Execute Account Expansion Strategy: Identify upsell and cross-sell opportunities within SLED customers to drive expansion revenue and long-term retention. — Stay Market-Aware: Maintain a strong understanding of cybersecurity trends, endpoint management, patch management, vulnerability remediation, public-sector IT priorities, and competitive positioning. — Operate with Ownership: Work independently in a remote-first environment while collaborating cross-functionally with Marketing, Channel, Customer Success, Product, and Sales Leadership. — Collaborate Cross-Functionally: Partner closely with marketing, product, channel, and sales leadership to incorporate field feedback and refine messaging for public-sector and education accounts. Requirements — 3–7+ years of quota-carrying B2B sales experience in SaaS, cybersecurity, IT infrastructure, endpoint management, or adjacent technical solutions. — Proven success consistently owning deals with a minimum average deal value of $30K. — Proven success selling into mid-market, enterprise, public sector, education, or other complex buying environments. — Experience engaging IT buyers, including IT Directors, SysAdmins, Security Leaders, Infrastructure teams, and technical stakeholders. — Demonstrated ability to generate pipeline and close complex deals. — Strong forecasting discipline and Salesforce hygiene. — Experience navigating longer, multi-stakeholder sales cycles, including technical validation, security review, procurement, and legal/commercial negotiation. — Excellent discovery and consultative selling skills. — Comfortable in a high-growth, fast-paced startup environment. — Ability to operate independently in a developing territory and create structure without relying on a fully mature sales motion. — We are seeking candidates who can start as soon as possible due to business needs. — SLED, public sector, education, government, or regulated-market sales experience. — Experience selling cybersecurity, endpoint management, patch management, vulnerability management, EDR, MDR, IAM, IT operations, or infrastructure solutions into public sector or education accounts. — Familiarity with public sector procurement processes, cooperative purchasing, contract vehicles, RFPs, reseller-led motions, or budget cycles. Benefits — A collaborative environment encouraging you to own your domain and implement best practices — Stable income, benefits, flexible working hours, and opportunities for promotion. — Friendly and professional peers, eager to help and help you grow. — A multitude of interesting challenges and opportunities.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Code RL at Anthropic drives reinforcement learning efforts behind Claude's coding capabilities, creating and scaling agentic coding environments. This is an engineering role with unusual latitude to set technical direction and standards. You'll be part of a team solving the engineering side of research efforts such as embedding with research teams, getting up to speed on their systems and needs, and designing the frameworks, APIs, and infrastructure that let researchers move faster, then rotating off, leaving behind well-oiled systems those teams can understand, own, and maintain themselves. Your remit also includes the ongoing health of production RL runs: maintainable, monitored, and straightforward to triage. The team's problem space spans the client side of sandboxed execution for agentic RL environments, large-scale data processing jobs, the lifecycle of production datasets, and the frameworks researchers build environments on. You won't own all of this yourself, you'll take on the slices where your depth matters most. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and hard-won intuition for how complex systems fail — especially silently. You should be comfortable diving into messy research code, finding the load-bearing abstractions, and improving them incrementally while researchers continue to build on top of your work. Key responsibilities Design widely-used APIs, frameworks, and abstractions that other engineers and researchers build on, with careful attention to interface legibility and principled defaults Embed with research teams on a rotational basis: understand their engineering needs, build systems and APIs that support their work, and transfer ownership so teams can maintain those systems after you rotate off Work directly in research codebases, improving reliability and structure without slowing down the research they support Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that shrink the surface area for bugs Contribute to the reliability of production RL systems, including monitoring, regression detection, and triage tooling Help define engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them Minimum qualifications Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant Python code A track record of designing intuitive, safe APIs or frameworks that other engineers or teams adopted and built on Experience working productively in large, evolving, or research-style codebases that you didn't originally write Demonstrated ability to anticipate failure modes — especially silent ones — and prevent them structurally through system design, type safety, and testing Strong written and verbal communication skills, including the ability to explain system designs to collaborators with varied engineering backgrounds Comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome Preferred qualifications Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows Familiarity with reinforcement learning concepts, agentic systems, or LLM training pipelines Experience building or operating large-scale distributed systems Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms Experience with large-scale data processing or dataset lifecycle management Experience designing plugin systems or extensible class hierarchies used across an organization Experience embedding with or consulting for other teams, including successfully handing off systems for others to own Experience defining code standards, lint rules, or static verification approaches adopted across multiple teams Prior experience as a technical lead, or setting engineering standards for a team Prior experience maintaining an open source project Representative projects These are examples of the challenges the team tackles; no one person will work on all of them: Design a base RL environment abstraction general enough to be subclassed across a wide range of environments Design a model-tool interface for sandboxed agentic environments that has explicit serialization semantics Partner with the platform teams that own the sandbox runtime to specify low-level features that improve the integrity of agentic coding tasks Design probes that catch sandbox regressions early Design the lifecycle and maintenance scheme for a production dataset Lead a research code refactor replacing loosely structured data containers with equivalents that carry stronger correctness guarantees, without breaking the experiments that depend on them Design lint rules and code-style requirements that favor statically verifiable patterns — including patterns less likely to be overlooked by an LLM reviewing or writing the code — to shrink the surface area for silent bugs Build the access layer that lets researchers discover and reuse data artifacts across teams The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $625,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Environments organization builds and maintains the infrastructure that improves Claude’s capabilities through reinforcement learning. That includes the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research. You'll embed with research teams, get up to speed on how they work, and design the frameworks and APIs that let them move faster, building systems the team can understand, own, and maintain themselves. Scope also includes keeping production RL runs healthy, maintainable, monitored, and easy to triage. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and good intuition for how complex systems fail, especially silently. It's a bonus if you've built and operated a stateful distributed system, such as a workflow engine, actor framework, or durable-execution runtime, where correctness depends on getting shared state and recovery right. You should be comfortable diving into messy research code, finding the abstractions that matter, and improving them incrementally while researchers continue to build on your work. You should also be comfortable using AI tools to accelerate your own development, but have an impulse towards deep verification. Key responsibilities Design widely used APIs, frameworks, and abstractions that other engineers and researchers build on, making correct usage the default and ruling out entire classes of errors structurally Own the platform layers that sit beneath every environment, including the agent runtime Build the tooling that lets environment owners understand, debug, and maintain their environments in production without needing an infrastructure engineer in the loop Embed with research teams on a rotational basis, work directly in their codebases without slowing down the research they support, and transfer ownership when you rotate off Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that reduce the room for correctness issues Drive adoption of new frameworks across the organization, including deprecations and cutovers Help define the engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them Minimum qualifications Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant code Strong taste in API and framework design, the ability to explain why an interface is right or wrong rather than just recognizing it, and a track record of other engineers or teams adopting and building on frameworks you have built Experience designing or operating stateful concurrent or distributed systems, and reasoning carefully about failure, retries, idempotency, and consistency A habit of verification: you measure before you conclude, and you build the checks that let a system show it's correct Experience working productively in large, evolving, or research-style codebases that you didn't originally write Strong written and verbal communication with collaborators of varied engineering backgrounds, and comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome Preferred qualifications Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows, and familiarity with agentic systems or LLM training pipelines Experience building agent frameworks, orchestration engines, or multi-agent systems, including checkpoint and restore, replay, and coordination of long-running stateful processes Experience using AI coding tools on code where correctness matters, with good judgment about what to delegate and how to make the results verifiable Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms Experience with large-scale data processing, dataset lifecycle management, or data lineage systems Experience designing serialization schemes, plugin systems, or extensible class hierarchies used across an organization Experience embedding with or consulting for other teams and handing off systems for others to own, or defining code standards adopted across teams, or prior experience as a technical lead Representative projects These are examples of the challenges the team tackles: Design a base RL environment abstraction that can be subclassed to support the large majority of environments built across RL Redesign the model-tool interface for sandboxed agentic environments so that state is guaranteed to survive serialization, making it structurally impossible to write a tool that silently loses state Design the state-sharing and recovery model for multi-agent workloads, so that losing a sandbox partway through a task becomes a transparent resume rather than lost work Define the failure and retry model for a sandboxed execution platform, distinguishing infrastructure faults from genuine task outcomes so that each is handled correctly Build the tooling that lets an environment owner diagnose why their environment is unhealthy in a production run, and fix it themselves The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $605,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.