{"has_next":true,"jobs":[{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","description":"About Anthropic \n 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.\n About the role \n As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","pre-training","agents","alignment","search","llm","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-06T17:15:29Z","expires_at":"2026-06-29T14:00:22.960238Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-05-30T14:00:23.075652Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8c6c621-b459-40f6-b41d-0baa191734ff"},{"id":"faa17528-2b03-4b12-9ce4-d471daa30ee2","company_id":"a0000000-0000-0000-0000-000000000001","title":"Engineering Manager, Cybersecurity Products","slug":"engineering-manager-cybersecurity-products-977bce2a","description":"About Anthropic \n 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.\n About Anthropic \n 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.\n About the role \n We are hiring an Engineering Manager to lead a team of engineers building AI-powered cybersecurity products. The work spans research, product, and go-to-market.\n Your team will prototype and ship products that use frontier models to defend code and infrastructure. You will set technical direction, partner with research to turn new model capabilities into products, and stay close to customers so the team builds the right things, not just builds things well.\n This is a builder's role. The team is small, the pace is high, and you should expect to be in the code, in customer calls, and in research reviews the same week. You also need to scale the team without losing the prototyping energy that got the product here.\n Responsibilities \n \n \n Lead and grow the team: hiring, performance, and the culture that keeps strong engineers doing their best work\n \n Set technical direction and sequence the roadmap across prototyping, enterprise hardening, and platform investments, with PM and PMM\n \n Partner with research to identify model capabilities worth productizing, and give research clear signal on the capability gaps blocking customer value\n \n Stay close to customers, design partners, and the security community; turn what you learn into product bets and unblock the team on the ones that matter\n \n Make architectural calls across agentic scanning pipelines, model orchestration, customer-facing surfaces, CI and source-control integrations, and the data infrastructure underneath\n \n Raise velocity by removing bottlenecks and sharpening operating rhythms, while holding the bar on quality, security, and reliability\n \n Coordinate with GTM, partnerships, and other product areas to land joint launches and ecosystem integrations\n \n Grow the next layer of leadership on the team so it can take on more as the charter expands\n \n You may be a good fit if you \n \n \n Have 8+ years of software engineering experience and 4+ years managing engineers, with ownership of a team's hiring, performance, and technical direction\n \n Have shipped cybersecurity products in production (SIEM, EDR, vulnerability management, application security, threat detection, incident response, or security automation)\n \n Have taken a team from prototype through first paying customers to scaled enterprise deployment\n \n Are technical and hands-on: comfortable in design reviews and in the team's code\n \n Have strong product instincts and a record of helping teams decide what to build, not just how\n \n Communicate clearly across functions and keep research, product, GTM, and executive partners aligned through ambiguity\n \n Treat direct customer contact as a primary input to your roadmap\n \n Care deeply about Anthropic's mission and about developing AI responsibly and safely\n \n Strong candidates may also have experience with \n \n \n Hands-on security expertise: application security, vulnerability research, reverse engineering, incident response, penetration testing, or detection engineering\n \n Building products on LLMs, including agentic systems, evals, and prompt and model iteration loops\n \n Strict data-handling environments (BYOC, CMEK, regulated industries, governments)\n \n Both startup and enterprise-scale company experience\n \n Working closely with research to translate capability into shipped product\n \n Ecosystem partnerships and MCP, CI/CD, or source-control integrations\n The annual compensation range for this role is listed below. \n 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.\n Annual Salary:\n $405,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n 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.\n Visa sponsorship:  We do sponsor visas! However, we aren't a","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["llm","security","alignment","agents"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5236531008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-30T03:37:09Z","expires_at":"2026-06-29T14:00:14.304228Z","created_at":"2026-05-30T14:00:14.410288Z","updated_at":"2026-05-30T14:00:14.410288Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/faa17528-2b03-4b12-9ce4-d471daa30ee2"},{"id":"ef3a3333-a3b2-413d-9313-7ffff60ec3fd","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Principal AI Engineer, Special Programs","slug":"principal-ai-engineer-special-programs-cdf31fea","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n PRINCIPAL AI ENGINEER, SPECIAL PROGRAMS \n This team focuses on engineering and deploying AI capabilities (models, APIs, tools, and integrations) for U.S. federal agencies. You'll work closely with product, research, infrastructure, and legal/governance teams to make  AI and future models maximally useful for missions while upholding safety, transparency, and ethical standards.\n RESPONSIBILITIES: \n \n Design, build, and optimize integrations between AI frontier models (e.g., Grok family) and government systems, platforms, and data environments\n Collaborate on custom SDKs, APIs, developer tools, and documentation tailored for government and enterprise developers\n Partner with agency stakeholders to understand requirements, prototype solutions, and iterate rapidly based on real-world feedback\n Ship production-grade code and features with a bias toward speed, simplicity, and measurable impact\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science or another STEM discipline; OR 2+ years of professional experience in software development in lieu of a degree\n 6+ years of hands-on software engineering experience building scalable systems, APIs, or AI/ML applications (strong Python proficiency, other languages a plus)\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Experience working with large language models, generative AI, or agentic systems—either in research, production, or applied engineering\n Familiarity with government or public sector technology environments (federal civilian agencies, state/local gov, or regulated industries like healthcare, finance, or infrastructure)\n Strong product sensibility: ability to translate ambiguous stakeholder needs into concrete technical solutions\n Demonstrated ability to write clean, maintainable, high-performance code under tight timelines\n Exceptional problem-solving skills and intellectual curiosity—you thrive on hard, ambiguous challenges\n Excellent communication skills; you can explain complex technical concepts to non-technical partners clearly and concisely\n Prior work on AI safety, governance, red-teaming, or responsible AI deployment\n Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker/Kubernetes), or API orchestration\n Background in policy-adjacent technical roles, civic tech, or public-interest technology\n Contributions to open-source AI projects or developer tools\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n 20% travel may be required to government sites\n This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment. While the clearance may not be immediately necessary upon hire, we encourage you to initiate the application process promptly upon accepting this offer. Your ability to secure the necessary clearance is essential for fulfilling key responsibilities of the role. Should you be unable to obtain it, SpaceX reserves the right to modify or terminate your employment to align with operational needs.\n \n COMPENSATION AND BENEFITS: \n Pay range:     Principal AI Engineer: $220,000.00 - $350,000.00/per year    \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .  \n \n SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced","salary_min":220000,"salary_max":350000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["generative-ai","alignment","llm","agents","healthcare"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8572113002?gh_jid=8572113002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T23:50:00Z","expires_at":"2026-06-29T14:16:59.183347Z","created_at":"2026-05-30T14:16:59.297291Z","updated_at":"2026-05-30T14:16:59.297291Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ef3a3333-a3b2-413d-9313-7ffff60ec3fd"},{"id":"09d0acb5-52de-4a76-88c6-0eb844785025","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - RL Training","slug":"research-scientist-rl-training-ffdbae39","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n ABOUT THE ROLE  \n We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models. This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering. \n You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data. \n MAIN RESPONSIBILITIES  \n \n Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models. \n Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks . \n Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries. \n Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.\n Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.\n Contribute to Snorkel's research publications and internal knowledge base in RL and model training.\n \n PREFERRED QUALIFICATIONS  \n \n Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work. \n Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure. \n Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL. \n Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows. \n Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus. \n Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints. \n Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered. \n Salary Range \n $200,000 — $325,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and haras","salary_min":200000,"salary_max":325000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["alignment","distributed-systems","pytorch","fine-tuning","generative-ai","data-pipeline","llm","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6009496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T21:22:40Z","expires_at":"2026-06-29T14:03:05.747327Z","created_at":"2026-05-30T14:03:05.857458Z","updated_at":"2026-05-30T14:03:05.857458Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/09d0acb5-52de-4a76-88c6-0eb844785025"},{"id":"a5365758-6ce5-460b-996c-bbaaa2aa3d6e","company_id":"a0000000-0000-0000-0000-000000000001","title":"Implementation Specialist ","slug":"implementation-specialist-35e895af","description":"About Anthropic \n 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.\n About the Role: \n We're building a team of Implementation Specialists to be the technical hands that switch Claude on inside our largest customers — the people who turn \"we've signed the contract\" into \"our employees can log in, with the right access, under the right policies, the way our security team expects.\" Before any CSM can drive adoption, before any Technical Specialist can run a workshop, before any champion can ship their first workflow, someone has to connect Claude to the customer's identity provider, get the right people provisioned into the right workspaces, and walk their CISO through how it all comes together. That's your work.\n This is not an adoption or enablement role — those sit with our Customer Success Managers and Technical Specialists. Your job is technical deployment, security credibility, and clean handoffs. You'll spend your time inside customer identity providers — Okta, Entra ID, Ping, Google Workspace — on working sessions with customer IT and IAM leads, debugging SAML assertions and SCIM attribute mappings, and authoring the runbooks that make every downstream hour of CS and Enablement investment compound instead of evaporate.\n You should be the kind of person a customer's security lead wants in the room — credible because you've shipped dozens of enterprise deployments, calm under pressure because you've debugged every flavor of IdP edge case, and trusted because when you say \"we can do that\" or \"we won't do that, here's why,\" your word holds.\n What you'll do: \n You'll typically run a portfolio of 5–10 concurrent enterprise deployments. A week looks roughly like:\n \n \n Configure and troubleshoot SSO (SAML, OIDC), SCIM/JIT provisioning, MCP connectors, admin policy templates, and seat-assignment logic across Okta, Entra ID, Ping, and Google Workspace observed as part of setup. Deliver informative transitions to Support for deeper issues that require investigation and/or engineering support\n \n Run technical working sessions with customer IT, IAM, and security leads — including CISOs at F500 scale — explaining our security model, shared-responsibility boundaries, and data-handling posture.\n \n Anticipate what a regulated customer's security review will ask (SOC 2, ISO 27001, HIPAA, FedRAMP) and partner with our AI Security Specialist when needed.\n \n Diagnose identity edge cases live on calls: broken SAML assertions, misconfigured SCIM attribute maps, group-filtering issues, tenant-provisioning quirks.\n \n Author Deployment Runbooks that the CSM inherits at handoff — as-built environment, admin contacts, known issues, support tier — so adoption starts warm.\n \n Contribute to repeatable playbooks and admin documentation that shape how the IS function scales — you'll be one of our first hires, so what you build becomes the template.\n \n What we're looking for: \n \n \n 5-7+ years in a customer-facing technical role — Technical Account Managers, Technical Onboarding, Technical Support, Solution Architects or technical consulting.\n \n 4+ years of hands-on, sustained ownership of enterprise identity integration work — SAML, OIDC, SCIM v2, JIT provisioning, across at least two major IdPs (Okta, Entra ID, Ping, Google Workspace) and familiarity with Work OS. You can debug a broken SAML assertion or a misconfigured SCIM attribute map without escalating.\n \n Senior candidates should have experience having led the Implementation Specialist function or built deployment playbooks from scratch at a prior company\n \n Proven track record running multiple concurrent enterprise deployments at F500 scale, with documented handoffs.\n \n Customer-facing technical communication skills that hold up under pressure. You can run a working session with a CISO and an IAM lead in the same room, explain trade-offs without jargon, and hold the line on scope without damaging the relationship.\n \n Working knowledge of enterprise security and compliance frameworks (SOC 2, ISO 27001, HIPAA, FedRAMP) sufficient to anticipate and answer customer security reviews.\n \n Experience in regulated industries: financial services, healthcare, pharma, public sector and with the security-review rigor they require.\n \n Portfolio discipline, you document as you go, you hit milestones you've committed to, and you don't drop balls across 5–10 concurrent threads.\n \n Prior experience at an enterprise SaaS or AI/ LLM vendor with a heavy admin/IdP surface (e.g., identity, collaboration, security, or productivity platforms).\n \n Exposure to AI/LLM products or agentic systems. \n \n Familiarity with Claude specific concepts like Skills, MCP, and Plugins.\n \n Experience partnering with SI / ","salary_min":151840,"salary_max":260000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["llm","agents","alignment","healthcare"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5233951008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T13:26:26Z","expires_at":"2026-06-29T14:00:16.996946Z","created_at":"2026-05-29T14:00:21.009787Z","updated_at":"2026-05-30T14:00:17.114841Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a5365758-6ce5-460b-996c-bbaaa2aa3d6e"},{"id":"999579f7-1c88-4dd4-b7e2-d60e2aada335","company_id":"e12d7a84-7538-4599-9b03-0cce91dc76b4","title":"AI Engineer","slug":"ai-engineer-b1ab9615","description":"GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.\n The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.\n * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. \n An overview of this role \n As an AI Engineer at GitLab, you'll help build the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you'll be a hands-on technical leader responsible for delivering internal AI-powered solutions that drive measurable business outcomes.\n Building fast matters, but it's not enough on its own. This role starts with understanding the real problem: mapping how work moves across teams, tools, and handoffs, identifying the true constraint, and validating whether AI is the right solution before you begin development. From there, you'll take ownership from discovery through deployment, combining strong engineering skills with systems thinking and business understanding.\n Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into key systems and workflows. This role offers the opportunity to shape how GitLab team members work, improve flow across the organization, and help advance our mission in a remote, asynchronous, and values-driven environment.\n What you'll do \n \n Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to say \"this doesn't need AI\" when that's the honest answer.\n Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.\n Design, develop, and ship AI-powered solutions quickly, delivering working prototypes in days, not months, with a focus on practical outcomes and measurable business value.\n Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.\n Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform, where appropriate. The right tool wins, whether that's custom code, a platform, or a well-crafted prompt.\n Be Customer Zero: leverage and showcase GitLab's AI offerings wherever possible, feeding real-world usage insights back to R\u0026D.\n Partner closely with stakeholders across functions to understand the real constraints. Ask the right questions, bridge technical and non-technical perspectives, and align on outcomes before jumping to solutions.\n Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.\n Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations that help the team scale its impact.\n \n What you'll bring \n \n A Technologist at Heart -  Genuinely invested in technology, the foundational and the cutting-edge in equal measure. You're as energised by a well-designed API integration as you are by the latest foundation model release. You reach for the simplest solution that solves the problem well, rather than forcing new technology when proven approaches would do. AI is a powerful part of your toolkit, but it sits on top of solid engineering fundamentals, not in place of them.\n Competent, Confident Coding Skills -  You can build working solutions end-to-end, write clean and maintainable code, and debug effectively. Whether your skills were honed in a traditional engineering role, through building automations, or shipping side projects, what matters is that you can deliver production-quality work independently.\n AI  \u0026 LLM  Technical Depth -  Strong proficiency in at least one modern scripting language (Pyth","salary_min":108400,"salary_max":129600,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"mid","tags":["alignment","llm","api-design","generative-ai","rag","agents"],"apply_url":"https://job-boards.greenhouse.io/gitlab/jobs/8565469002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T11:49:29Z","expires_at":"2026-06-29T14:08:37.633939Z","created_at":"2026-05-29T14:32:31.013898Z","updated_at":"2026-05-30T14:08:37.751307Z","company_name":"GitLab","company_slug":"gitlab","company_logo_url":"https://www.google.com/s2/favicons?domain=about.gitlab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/999579f7-1c88-4dd4-b7e2-d60e2aada335"},{"id":"bcb70829-4113-4c0a-905d-63b4f11d19bc","company_id":"e12d7a84-7538-4599-9b03-0cce91dc76b4","title":"Senior AI Engineer","slug":"senior-ai-engineer-a1be6a92","description":"GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.\n The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.\n * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. \n \n An overview of this role \n As a Senior AI Engineer at GitLab, you'll help build the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you'll be a hands-on technical leader responsible for delivering internal AI-powered solutions that drive measurable business outcomes.\n Building fast matters, but it's not enough on its own. This role starts with understanding the real problem: mapping how work moves across teams, tools, and handoffs, identifying the true constraint, and validating whether AI is the right solution before you begin development. From there, you'll take ownership from discovery through deployment, combining strong engineering skills with systems thinking and business understanding.\n Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into key systems and workflows. This role offers the opportunity to shape how GitLab team members work, improve flow across the organization, and help advance our mission in a remote, asynchronous, and values-driven environment.\n What you'll do \n \n Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to say \"this doesn't need AI\" when that's the honest answer.\n Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.\n Design, develop, and ship AI-powered solutions quickly, delivering working prototypes in days, not months, with a focus on practical outcomes and measurable business value.\n Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.\n Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform, where appropriate. The right tool wins, whether that's custom code, a platform, or a well-crafted prompt.\n Be Customer Zero: leverage and showcase GitLab's AI offerings wherever possible, feeding real-world usage insights back to R\u0026D.\n Partner closely with stakeholders across functions to understand the real constraints. Ask the right questions, bridge technical and non-technical perspectives, and align on outcomes before jumping to solutions.\n Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.\n Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations that help the team scale its impact.\n \n What you'll bring \n \n A Technologist at Heart -  Genuinely invested in technology, the foundational and the cutting-edge in equal measure. You're as energised by a well-designed API integration as you are by the latest foundation model release. You reach for the simplest solution that solves the problem well, rather than forcing new technology when proven approaches would do. AI is a powerful part of your toolkit, but it sits on top of solid engineering fundamentals, not in place of them.\n Competent, Confident Coding Skills -  You can build working solutions end-to-end, write clean and maintainable code, and debug effectively. Whether your skills were honed in a traditional engineering role, through building automations, or shipping side projects, what matters is that you can deliver production-quality work independently.\n AI  \u0026 LLM  Technical Depth -  Strong proficiency in at least one modern scripting lan","salary_min":139200,"salary_max":218400,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","agents","api-design","rag","alignment"],"apply_url":"https://job-boards.greenhouse.io/gitlab/jobs/8548545002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-25T22:37:15Z","expires_at":"2026-06-29T14:08:38.410651Z","created_at":"2026-05-27T14:08:52.435541Z","updated_at":"2026-05-30T14:08:38.523476Z","company_name":"GitLab","company_slug":"gitlab","company_logo_url":"https://www.google.com/s2/favicons?domain=about.gitlab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bcb70829-4113-4c0a-905d-63b4f11d19bc"},{"id":"e4b39a7b-4774-4c89-9bae-743612240ae7","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Sr. AI Engineer, Special Programs","slug":"sr-ai-engineer-special-programs-a5dcf83f","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n SR. AI ENGINEER, SPECIAL PROGRAMS \n This team focuses on engineering and deploying AI capabilities (models, APIs, tools, and integrations) for U.S. federal agencies. You'll work closely with product, research, infrastructure, and legal/governance teams to make  AI and future models maximally useful for missions while upholding safety, transparency, and ethical standards.\n RESPONSIBILITIES: \n \n Design, build, and optimize integrations between AI frontier models (e.g., Grok family) and government systems, platforms, and data environments\n Collaborate on custom SDKs, APIs, developer tools, and documentation tailored for government and enterprise developers\n Partner with agency stakeholders to understand requirements, prototype solutions, and iterate rapidly based on real-world feedback\n Ship production-grade code and features with a bias toward speed, simplicity, and measurable impact\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science or another STEM discipline; OR 2+ years of professional experience in software development in lieu of a degree\n 5+ years of hands-on software engineering experience building scalable systems, APIs, or AI/ML applications (strong Python proficiency, other languages a plus)\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Experience working with large language models, generative AI, or agentic systems—either in research, production, or applied engineering\n Familiarity with government or public sector technology environments (federal civilian agencies, state/local gov, or regulated industries like healthcare, finance, or infrastructure)\n Strong product sensibility: ability to translate ambiguous stakeholder needs into concrete technical solutions\n Demonstrated ability to write clean, maintainable, high-performance code under tight timelines\n Exceptional problem-solving skills and intellectual curiosity—you thrive on hard, ambiguous challenges\n Excellent communication skills; you can explain complex technical concepts to non-technical partners clearly and concisely\n Prior work on AI safety, governance, red-teaming, or responsible AI deployment\n Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker/Kubernetes), or API orchestration\n Background in policy-adjacent technical roles, civic tech, or public-interest technology\n Contributions to open-source AI projects or developer tools\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n 20% travel may be required to government sites\n This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment. While the clearance may not be immediately necessary upon hire, we encourage you to initiate the application process promptly upon accepting this offer. Your ability to secure the necessary clearance is essential for fulfilling key responsibilities of the role. Should you be unable to obtain it, SpaceX reserves the right to modify or terminate your employment to align with operational needs.\n \n COMPENSATION AND BENEFITS: \n Pay range:     Sr. AI Engineer: $220,000.00 - $350,000.00/per year    \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .  \n \n SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any mann","salary_min":220000,"salary_max":350000,"location":"Washington, DC","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["llm","generative-ai","healthcare","alignment","agents"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8557268002?gh_jid=8557268002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T20:09:08Z","expires_at":"2026-06-29T14:16:59.834205Z","created_at":"2026-05-27T14:17:47.840002Z","updated_at":"2026-05-30T14:16:59.945856Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e4b39a7b-4774-4c89-9bae-743612240ae7"},{"id":"f0b92648-a73d-40b6-a5a3-a3d13ec10e7f","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Sr. AI Engineer, Special Programs","slug":"sr-ai-engineer-special-programs-d39656bd","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n SR. AI ENGINEER, SPECIAL PROGRAMS \n This team focuses on engineering and deploying AI capabilities (models, APIs, tools, and integrations) for U.S. federal agencies. You'll work closely with product, research, infrastructure, and legal/governance teams to make  AI and future models maximally useful for missions while upholding safety, transparency, and ethical standards.\n RESPONSIBILITIES: \n \n Design, build, and optimize integrations between AI frontier models (e.g., Grok family) and government systems, platforms, and data environments\n Collaborate on custom SDKs, APIs, developer tools, and documentation tailored for government and enterprise developers\n Partner with agency stakeholders to understand requirements, prototype solutions, and iterate rapidly based on real-world feedback\n Ship production-grade code and features with a bias toward speed, simplicity, and measurable impact\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science or another STEM discipline; OR 2+ years of professional experience in software development in lieu of a degree\n 5+ years of hands-on software engineering experience building scalable systems, APIs, or AI/ML applications (strong Python proficiency, other languages a plus)\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Experience working with large language models, generative AI, or agentic systems—either in research, production, or applied engineering\n Familiarity with government or public sector technology environments (federal civilian agencies, state/local gov, or regulated industries like healthcare, finance, or infrastructure)\n Strong product sensibility: ability to translate ambiguous stakeholder needs into concrete technical solutions\n Demonstrated ability to write clean, maintainable, high-performance code under tight timelines\n Exceptional problem-solving skills and intellectual curiosity—you thrive on hard, ambiguous challenges\n Excellent communication skills; you can explain complex technical concepts to non-technical partners clearly and concisely\n Prior work on AI safety, governance, red-teaming, or responsible AI deployment\n Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker/Kubernetes), or API orchestration\n Background in policy-adjacent technical roles, civic tech, or public-interest technology\n Contributions to open-source AI projects or developer tools\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n 20% travel may be required to government sites\n This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment. While the clearance may not be immediately necessary upon hire, we encourage you to initiate the application process promptly upon accepting this offer. Your ability to secure the necessary clearance is essential for fulfilling key responsibilities of the role. Should you be unable to obtain it, SpaceX reserves the right to modify or terminate your employment to align with operational needs.\n \n COMPENSATION AND BENEFITS: \n Pay range:     Sr. AI Engineer: $220,000.00 - $350,000.00/per year    \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .  \n \n SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any mann","salary_min":220000,"salary_max":350000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["llm","alignment","agents","generative-ai","healthcare"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8557060002?gh_jid=8557060002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T20:09:07Z","expires_at":"2026-06-29T14:16:59.752092Z","created_at":"2026-05-27T14:17:47.924353Z","updated_at":"2026-05-30T14:16:59.86861Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f0b92648-a73d-40b6-a5a3-a3d13ec10e7f"},{"id":"4f4a4c03-3ec2-4710-b84b-140c8a90c3c8","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"AI Engineer, Special Programs","slug":"ai-engineer-special-programs-47bfcf84","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n AI ENGINEER, SPECIAL PROGRAMS \n This team focuses on engineering and deploying AI capabilities (models, APIs, tools, and integrations) for U.S. federal agencies. You'll work closely with product, research, infrastructure, and legal/governance teams to make  AI and future models maximally useful for missions while upholding safety, transparency, and ethical standards.\n RESPONSIBILITIES: \n \n Design, build, and optimize integrations between AI frontier models (e.g., Grok family) and government systems, platforms, and data environments\n Collaborate on custom SDKs, APIs, developer tools, and documentation tailored for government and enterprise developers\n Partner with agency stakeholders to understand requirements, prototype solutions, and iterate rapidly based on real-world feedback\n Ship production-grade code and features with a bias toward speed, simplicity, and measurable impact\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science or another STEM discipline; OR 2+ years of professional experience in software development in lieu of a degree\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n 1+ years of hands-on software engineering experience building scalable systems, APIs, or AI/ML applications (strong Python proficiency, other languages a plus)\n Experience working with large language models, generative AI, or agentic systems—either in research, production, or applied engineering\n Familiarity with government or public sector technology environments (federal civilian agencies, state/local gov, or regulated industries like healthcare, finance, or infrastructure)\n Strong product sensibility: ability to translate ambiguous stakeholder needs into concrete technical solutions\n Demonstrated ability to write clean, maintainable, high-performance code under tight timelines\n Exceptional problem-solving skills and intellectual curiosity—you thrive on hard, ambiguous challenges\n Excellent communication skills; you can explain complex technical concepts to non-technical partners clearly and concisely\n Prior work on AI safety, governance, red-teaming, or responsible AI deployment\n Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker/Kubernetes), or API orchestration\n Background in policy-adjacent technical roles, civic tech, or public-interest technology\n Contributions to open-source AI projects or developer tools\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n 20% travel may be required to government sites\n This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment. While the clearance may not be immediately necessary upon hire, we encourage you to initiate the application process promptly upon accepting this offer. Your ability to secure the necessary clearance is essential for fulfilling key responsibilities of the role. Should you be unable to obtain it, SpaceX reserves the right to modify or terminate your employment to align with operational needs.\n \n COMPENSATION AND BENEFITS: \n Pay range:     AI Engineer: $125,000.00 - $220,000.00/per year    \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .  \n \n SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by ra","salary_min":125000,"salary_max":220000,"location":"Washington, DC","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["llm","generative-ai","healthcare","alignment","agents"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8557208002?gh_jid=8557208002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T20:05:30Z","expires_at":"2026-06-29T14:16:57.882192Z","created_at":"2026-05-27T14:17:46.716214Z","updated_at":"2026-05-30T14:16:58.004111Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4f4a4c03-3ec2-4710-b84b-140c8a90c3c8"},{"id":"315e7114-4768-4f85-b9c5-821137e4cad5","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"AI Engineer, Special Programs","slug":"ai-engineer-special-programs-db463890","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n AI ENGINEER, SPECIAL PROGRAMS \n This team focuses on engineering and deploying AI capabilities (models, APIs, tools, and integrations) for U.S. federal agencies. You'll work closely with product, research, infrastructure, and legal/governance teams to make  AI and future models maximally useful for missions while upholding safety, transparency, and ethical standards.\n RESPONSIBILITIES: \n \n Design, build, and optimize integrations between AI frontier models (e.g., Grok family) and government systems, platforms, and data environments\n Collaborate on custom SDKs, APIs, developer tools, and documentation tailored for government and enterprise developers\n Partner with agency stakeholders to understand requirements, prototype solutions, and iterate rapidly based on real-world feedback\n Ship production-grade code and features with a bias toward speed, simplicity, and measurable impact\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science or another STEM discipline; OR 2+ years of professional experience in software development in lieu of a degree\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n 1+ years of hands-on software engineering experience building scalable systems, APIs, or AI/ML applications (strong Python proficiency, other languages a plus)\n Experience working with large language models, generative AI, or agentic systems—either in research, production, or applied engineering\n Familiarity with government or public sector technology environments (federal civilian agencies, state/local gov, or regulated industries like healthcare, finance, or infrastructure)\n Strong product sensibility: ability to translate ambiguous stakeholder needs into concrete technical solutions\n Demonstrated ability to write clean, maintainable, high-performance code under tight timelines\n Exceptional problem-solving skills and intellectual curiosity—you thrive on hard, ambiguous challenges\n Excellent communication skills; you can explain complex technical concepts to non-technical partners clearly and concisely\n Prior work on AI safety, governance, red-teaming, or responsible AI deployment\n Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker/Kubernetes), or API orchestration\n Background in policy-adjacent technical roles, civic tech, or public-interest technology\n Contributions to open-source AI projects or developer tools\n \n ADDITIONAL REQUIREMENTS: \n \n Must be willing to work extended hours and weekends as needed\n 20% travel may be required to government sites\n This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment. While the clearance may not be immediately necessary upon hire, we encourage you to initiate the application process promptly upon accepting this offer. Your ability to secure the necessary clearance is essential for fulfilling key responsibilities of the role. Should you be unable to obtain it, SpaceX reserves the right to modify or terminate your employment to align with operational needs.\n \n COMPENSATION AND BENEFITS: \n Pay range:     AI Engineer: $125,000.00 - $220,000.00/per year    \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewards package at SpaceX. You may also be eligible for long-term incentives, in the form of company stock, stock options, or long-term cash awards, as well as potential discretionary bonuses and the ability to purchase additional stock at a discount through an Employee Stock Purchase Plan. You will also receive access to comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, paid parental leave, and various other discounts and perks. You may also accrue 3 weeks of paid vacation and will be eligible for 10 or more paid holidays per year. Employees accrue paid sick leave pursuant to Company policy which satisfies or exceeds the accrual, carryover, and use requirements of the law.\n ITAR REQUIREMENTS: \n \n To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .  \n \n SpaceX is an Equal Opportunity Employer; employment with SpaceX is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by ra","salary_min":125000,"salary_max":220000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["alignment","agents","llm","generative-ai","healthcare"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8557038002?gh_jid=8557038002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T20:05:29Z","expires_at":"2026-06-29T14:16:57.974092Z","created_at":"2026-05-27T14:17:46.806655Z","updated_at":"2026-05-30T14:16:58.082438Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/315e7114-4768-4f85-b9c5-821137e4cad5"},{"id":"a62cc613-162e-4779-81ca-502537d39185","company_id":"a0000000-0000-0000-0000-000000000001","title":"Performance Engineer, Inference Systems","slug":"performance-engineer-inference-systems-d02d5600","description":"About Anthropic \n 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.\n About the Role \n Anthropic's inference fleet serves Claude to millions of users across our own products and the world's largest cloud platforms. The stack that makes this possible is deep and tightly coupled: accelerator kernels, model servers, distributed routing, autoscaling, capacity management. Every layer affects the others, often in ways that are hard to see in isolation.\n The Inference System Dynamics team is responsible for understanding that whole system and holding it to a high bar across four dimensions: throughput, latency, reliability, and correctness . We measure how the fleet performs against its theoretical performance frontier, run cross-layer investigations to explain the gaps, and own the correctness checks that make sure Claude's outputs are right, not just fast, across hardware platforms and serving configurations. We don't own the individual components. We instrument and model them, find the highest-leverage opportunities across them, and partner with the owning teams to land the wins.\n You'll work across all four areas. One week that might mean tracing a tail-latency regression from request timing down through routing and batching into a kernel overhead; the next it might mean tightening a correctness eval so it catches an output regression introduced by a quantization change. We're looking for performance engineers who treat correctness as part of performance.\n Key Responsibilities \n \n Run cross-layer performance investigations across throughput, latency, and reliability, sizing the gap between actual fleet performance and theoretical rooflines, identifying root causes, and quantifying the value of closing them\n Own and improve the correctness evaluation pipeline that validates model output quality across hardware platforms, numerics, and serving configurations, and lead the investigation when it catches a regression\n Build the observability, dashboards, and modeling tools that make throughput, latency, cost, reliability, correctness, and their interactions legible across the stack\n Partner with kernel, serving, routing, autoscaling, and capacity teams to prioritize and land the highest-impact optimizations your analysis surfaces\n Ruthlessly stack-rank a large surface area of opportunities by impact and effort, and say no to the ones that don't make the cut\n \n Minimum Qualifications \n \n Hands-on performance engineering experience: profiling, roofline analysis, latency/throughput optimization, and root-cause investigation in complex production systems\n Proficiency in Python, with the ability to read, instrument, and contribute to large production codebases you didn’t write\n Solid data analysis skills (e.g. SQL, pandas, or similar) sufficient to turn raw telemetry into clear findings\n Ability to communicate quantitative results clearly in writing to influence priorities on teams you don't manage\n Genuine interest in correctness as an engineering discipline: numerics, evaluation design, regression detection\n \n Preferred Qualifications \n \n Experience with ML systems, especially training or inference infrastructure or general LLM serving stacks. Direct large-scale inference experience is a strong plus\n Familiarity with GPU/TPU/accelerator performance concepts (memory bandwidth, kernel overheads, quantization, collective communication). Reasoning about these matters more than having written kernels yourself\n Experience with reliability engineering for high-throughput services: autoscaling, load balancing, request routing, tail latency\n Experience with model evaluation or numerical regression-detection pipelines\n Experience building observability or telemetry for distributed systems\n Comfortable having impact through influence and evidence rather than direct ownership\n \n Representative Projects \n \n Trace a 350ms latency gap on a new accelerator platform from end-to-end request timing down to a server scheduling overhead, quantify the win, and land the fix directly or with the owning team\n Redesign the correctness eval gate: determine which signals reliably catch real model-output regressions versus noise, and make it the trusted release criterion across hardware backends\n Build a FLOPs funnel that breaks down where compute actually goes across the fleet, exposing the gap between achieved throughput and kernel rooflines\n Root-cause a numerical divergence between two hardware platforms to a specific kernel change, and define the acceptance threshold going forward\n Model the latency–cost impact of changing batch-sizing and utilization targets, and turn the result into the signal the autoscaler uses in production\n \n Deadline to apply: None. Applications ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","alignment","llm","research","inference"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5224564008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T22:53:11Z","expires_at":"2026-06-29T14:00:19.065435Z","created_at":"2026-05-27T14:00:24.711949Z","updated_at":"2026-05-30T14:00:19.17401Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a62cc613-162e-4779-81ca-502537d39185"},{"id":"9f4195d7-f223-4644-bba5-da9c9e66f839","company_id":"a0000000-0000-0000-0000-000000000001","title":"Product Manager, Developer Productivity","slug":"product-manager-developer-productivity-48e76021","description":"About Anthropic \n 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.\n About the role \n As a Product Manager focused on Developer Productivity, you'll partner with Infrastructure, Inference, Research, and Product Engineering to build the systems that determine how thousands of engineers and researchers at Anthropic develop, build, test, and ship code—the foundation on which every model, evaluation, and product feature depends:\n \n Partner with Developer Productivity engineering teams to own the end-to-end developer experience—from the source control and language ecosystems that underpin our monorepo, to the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers, to the acceleration tooling that deeply integrates Claude into every engineer's workflow.\n Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence.\n You'll drive the evolution of our developer platform through a fundamental shift in how software gets built—as AI agents move from autocomplete to autonomous collaborators, the definition of \"developer\" is changing, and our tooling, governance, and workflows must change with it. You'll be defining what developer productivity means when a meaningful share of code is written, tested, and reviewed by Claude itself.\n You will define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer that makes Anthropic the most productive place in the world to build frontier AI.\n \n Responsibilities: \n \n Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product—from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints.\n Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code—including how we measure productivity when the unit of work is no longer a human typing.\n Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount—ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck.\n Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems.\n Own the trade-off framework between velocity, reliability, security, and cost—making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership.\n Establish and champion the productivity metrics that matter in an AI-native engineering org—moving beyond commits and cycle time to measures that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship.\n Build conviction about where developer tooling is headed on a 2–3 year horizon, and translate that into a roadmap that keeps Anthropic ahead of—not reacting to—the exponential curve of AI-assisted development.\n \n You may be a good fit if you have:\n \n 7+ years of product management experience, with deep exposure to developer tooling, build systems, CI/CD, or platform infrastructure\n Experience taking technical platform products from infancy to scale—you've built something from the ground up and grown it to serve demanding internal or external engineering customers\n Track record of building platform products that balance the needs of multiple engineering personas—you're comfortable making prioritization trade-offs between velocity, reliability, and security, and communicating them clearly\n Ability to internalize complex technical systems (build systems, monorepos, CI pipelines, accelerator toolchains) and translate that understanding into a comprehensive product vision\n Fluent across functions—you're equally credible discussing build graph optimization with engineers, developer velocity economics with leadership, and AI-agent governance with security teams\n A strong thesis on how AI will reshape software development—you've thought deeply about what changes when agent","salary_min":385000,"salary_max":595000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["agents","cloud","alignment","gpu","code-generation"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5220143008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T10:34:22Z","expires_at":"2026-06-29T14:00:19.557033Z","created_at":"2026-05-19T14:00:21.471137Z","updated_at":"2026-05-30T14:00:19.669103Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9f4195d7-f223-4644-bba5-da9c9e66f839"},{"id":"cdb4264b-9975-4524-b6bd-3c0e216f6177","company_id":"a0000000-0000-0000-0000-000000000003","title":"Research Scientist, Safety Post Training","slug":"research-scientist-safety-post-training-c518e4c9","description":"Scale Labs, Research Scientist — Safety Post Training \n As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.\n Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.\n As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer, and better understood by researchers and policymakers.. For example, you might: \n \n Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties;\n Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations;\n Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices.\n \n  \n Ideally you’d have: \n \n Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.\n Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.\n A track record of published research in machine learning, particularly in generative AI.\n At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.\n Strong written and verbal communication skills to operate in a cross-functional team.\n \n Nice to have: \n \n Experience with mechanistic interpretability, probing, or other techniques for understanding model internals.\n Familiarity with red-teaming or adversarial evaluation of post-trained models.\n Experience studying failure modes introduced or masked by post-training, such as reward hacking, sycophancy, or alignment faking.\n \n Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $216,000 — $270,000 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army a","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["alignment","reinforcement-learning","generative-ai","research"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4696595005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-18T19:54:26Z","expires_at":"2026-06-29T14:01:12.598488Z","created_at":"2026-05-19T14:01:22.953727Z","updated_at":"2026-05-30T14:01:12.708729Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cdb4264b-9975-4524-b6bd-3c0e216f6177"},{"id":"5cfb40f9-9a98-41d6-a73c-1d5f56b271b4","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Safeguards Labs","slug":"pipeline-software-engineer-safeguards-labs-04cf0a54","description":"About Anthropic \n 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.\n About the role \n Safeguards Labs is a new team operating at the intersection of research and engineering, chartered to investigate novel safety methods that protect Claude and the people who use it. We prototype new approaches to safe models, usage safeguards, and production safety, pressure-testing ideas before they graduate into production systems run by our partner Safeguards teams.\n We're hiring software engineers to partner with our research engineers and turn promising prototypes into reliable, production-grade safeguards. The team is small, so each engineer has substantial latitude over what they work on and high leverage on the team's direction.\n Key responsibilities \n \n \n Take research prototypes and harden them into production services that integrate with Anthropic's real-time safeguards path.\n \n Build data and evaluation infrastructure that lets the team iterate on prototypes quickly and measure whether safeguards actually work, including in agentic settings.\n \n Own deployment, monitoring, and reliability for systems Labs ships.\n \n Build internal tooling that helps investigators surface and act on abuse patterns.\n \n Collaborate with research engineers on scoping and contribute to decisions about which prototypes are ready to graduate.\n \n Minimum qualifications \n \n \n Strong proficiency in Python and comfort working with large datasets.\n \n A track record of designing, building, and operating production backend systems or data pipelines.\n \n Experience taking software from prototype to production, including testing, monitoring, and reliability work.\n \n Working familiarity with how large language models operate, even if LLMs aren't your primary background.\n \n Care about the societal impacts of AI and want your work to directly reduce real-world harm.\n \n Preferred qualifications \n \n \n At least 5 years of software engineering experience.\n \n Experience deploying ML systems or classifiers into production.\n \n Background in trust and safety, integrity, fraud detection, threat intelligence, or adversarial ML.\n \n Experience building developer-facing tooling or platforms that accelerate research workflows.\n \n Familiarity with evaluation methodologies for language models.\n \n Experience with agentic environments.\n \n A history of partnering with researchers and successfully transferring prototypes into production.\n The annual compensation range for this role is listed below. \n 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.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n 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.\n 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.\n 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'","salary_min":320000,"salary_max":485000,"location":"New York, NY","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["llm","agents","search","data-pipeline","alignment","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5219486008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-14T19:21:56Z","expires_at":"2026-06-29T14:00:25.498086Z","created_at":"2026-05-15T14:00:22.117Z","updated_at":"2026-05-30T14:00:25.614545Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5cfb40f9-9a98-41d6-a73c-1d5f56b271b4"},{"id":"c92a34c5-38f3-4162-b7f3-a5e5c8ab11f7","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Cloud Inference Launch Engineering","slug":"staff-sr-software-engineer-cloud-inference-launch-engineering-e1f8fd98","description":"About Anthropic \n 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.\n About the Role\n The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.\n Within Cloud Inference, the model \u0026 inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.\n This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.\n What You'll Do\n \n Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform\n Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production\n Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds\n Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production\n Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability \n Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads\n \n You May Be a Good Fit If You:\n \n Have a strong interest in LLM serving; prior inference or ML experience is not required \n Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users\n Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability\n Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration\n Thrive in cross-functional collaboration with both internal teams and external partners\n Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems\n Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description\n \n Strong Candidates May Also Have Experience With:\n \n LLM inference optimization, batching, and caching strategies\n Capacity-constrained scheduling or shared-resource test infrastructure\n Solid understanding of multi-region deployments, request routing, load balancing, global traffic management\n Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service\n Proficiency in Python or Rust\n The annual compensation range for this role is listed below. \n 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.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the posit","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["alignment","distributed-systems","llm","inference","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5215028008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T21:07:33Z","expires_at":"2026-06-29T14:00:28.188979Z","created_at":"2026-05-10T14:00:33.138268Z","updated_at":"2026-05-30T14:00:28.301764Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c92a34c5-38f3-4162-b7f3-a5e5c8ab11f7"},{"id":"7e2fdf25-4b9e-44d0-a5b8-58126ec4cb9f","company_id":"e455f75a-a424-4955-9844-afebe8ea6eb4","title":"Principal Software Engineer, AI (Web \u0026 Data)","slug":"principal-software-engineer-ai-web-data-8006d011","description":"Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.\n  \n Role Overview  \n We are seeking an accomplished Principal Engineer to lead the technical architecture and evolution of our hybrid digital ecosystem. In this pivotal role, you will drive the strategic integration of multi-modal AI into our core infrastructure while architecting an omnichannel platform capable of supporting complex customer journeys. You will balance the adoption of proven third-party AI solutions (OpenAI, Anthropic) with the development of proprietary optimizations where they deliver a competitive advantage. As a technical leader, you will champion a vendor-agnostic, ethically grounded, and Privacy by Design approach to AI implementation.\n Key Responsibilities \n \n Infrastructure Modernization: Lead the roadmap for migrating legacy data pipelines to AI-native architectures. You will design modern data orchestration solutions utilizing Apache Airflow, dbt, and Kafka to replace outdated batch processing with real-time, event-driven flows.\n AI-Native Web Transformation: Define the web infrastructure required for a high-agility ecosystem. Architect the evolution of our Headless CMS environments (AEM, Contentful) and modern frontend frameworks to enable automated page assembly and AI-driven UI components.\n Agentic Pipeline Development: Design and build autonomous pipelines that bridge the gap between design systems, automated component development, and publishing workflows.\n ML \u0026 Data Engineering Leadership: Establish standards for RAG, vector databases, and LLM orchestration. Provide architectural guidance for the seamless integration of AI capabilities across headless and omnichannel systems while ensuring consistency, performance, and security.\n Engineering Productivity: Champion the use of AI-assisted coding and engineering productivity tools such as Claude Code and Cursor to accelerate development cycles and optimize architectural decision-making.\n \n Required Qualifications \n \n Experience: 8–10 years in software engineering, specializing in SaaS platform architecture and distributed systems design.\n Leadership \u0026 Advocacy: Excellence in technical diplomacy and stakeholder influence. Proven ability to translate complex AI/Data roadmaps into business value while mentoring senior teams and advocating for architectural best practices.\n Data Orchestration: Strong hands-on experience with Apache Airflow (or similar), Kafka, and dbt to support real-time AI applications.\n AI \u0026 Web Convergence: Proven expertise integrating LLMs into production web environments with a focus on agentic workflows and autonomous UI generation.\n Architectural Vision: Deep understanding of headless CMS, composable, and event-driven patterns that allow for programmatic content and UI generation.\n Modern Delivery: Experience with cloud-native technologies (AWS/GCP, Docker, Kubernetes) and a deep expertise in data privacy frameworks and AI ethics.\n \n \n  \n #LI-Hybrid\n #P24079_3413893\n Below is the annual salary range for candidates located in Canada. Your actual salary will depend on factors such as your skills, qualifications, and experience. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental, and vision insurance, RRSP with a match, healthcare spending, telemedicine, and paid leave (including PTO and parental leave) in accordance with our applicable plans and policies. To learn more about our Total Rewards program, please visit:  https://rewards.okta.com/can .\n The annual base salary range for this position for candidates located in Canada is between:\n $175,000 — $192,500 CAD \n The Okta Experience \n \n Supporting Your Well-Being  \n Driving Social Impact  \n Developing Talent and Fostering Connection + Community \n \n We are intentional about connection. Our global community, spanning over 20 offices worldwide, is united by a drive to innovate. Your journey begins with an immersive, in-person onboarding experience designed to accelerate your impact and connect you to our mission and team from day one. Okta is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws. If reasonable accommodation is needed to comp","salary_min":175000,"salary_max":192500,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["llm","cloud","data-pipeline","alignment","healthcare","embeddings","distributed-systems","agents"],"apply_url":"https://www.okta.com/company/careers/opportunity/7819118?gh_jid=7819118","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T17:19:56Z","expires_at":"2026-06-29T14:09:00.073135Z","created_at":"2026-05-10T14:10:14.736972Z","updated_at":"2026-05-30T14:09:00.183858Z","company_name":"Okta","company_slug":"okta","company_logo_url":"https://www.google.com/s2/favicons?domain=okta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7e2fdf25-4b9e-44d0-a5b8-58126ec4cb9f"},{"id":"f8473e70-d698-4d8a-8e2b-7201b8cca131","company_id":"a0000000-0000-0000-0000-000000000001","title":"Data Scientist, Supply","slug":"data-scientist-supply-6a012908","description":"About Anthropic \n 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.\n About the role\n Anthropic is compute-constrained, and how we allocate that compute is one of the highest-leverage decisions we make as a company. Today, those allocation choices are only loosely tied to the user outcomes we ultimately care about — retention, lifetime value, and the experience of people relying on Claude. You will change that.\n As a hands-on technical IC on the Supply pillar of our Data Science \u0026 Analytics team, you'll sit alongside the infrastructure engineers who run our compute and help decide how our scarcest resource gets used. You'll design and run the analyses, observational and synthetic experiments, and optimization frameworks that turn opaque supply decisions into shared, measurable understanding across the company. Your work will directly shape how frontier AI reaches the world at scale, and your findings will go in front of senior leadership, including our CTO and his staff.\n This role is a fit for someone who thinks natively in terms of constrained allocation and queueing, who enjoys getting close to the system rather than analyzing it from a distance, and who wants their analyses to translate into operational changes that ship.\n Responsibilities:\n \n Build and run testing frameworks — observational and synthetic — to quantify how different inputs affect compute allocation outcomes\n Connect compute allocation decisions to downstream user outcomes (retention, LTV, revenue) so we stop optimizing in a vacuum\n Partner closely with infrastructure engineers, product, and research to instrument the system, measure what matters, and ship operational changes\n Develop the metric hierarchies, dashboards, and reporting that turn supply decisions into shared understanding across the company\n Contribute analyses and recommendations to executive forums, and co-author the supply narrative the team takes to the CTO and his staff\n Measure and improve how AI affects developer productivity inside Anthropic\n \n You may be a good fit if you have:\n \n Strong technical IC background in data science, analytics, or operations research\n Operations research foundation — you think natively in terms of optimization, constrained allocation, and queueing\n Deep proficiency with Python, SQL, and data visualization tools\n Track record of owning analyses end-to-end and communicating results clearly to engineering and product leadership\n Direct experience working closely with engineering teams on production systems\n A passion for Anthropic's mission of building helpful, honest, and harmless AI\n \n Strong candidates may have: \n \n 6+ years of technical IC experience in data science, analytics, or operations research; 8+ years for candidates targeting a Staff-level scope\n Experience with highly complex systems with many interacting components (ad networks, payment processing, marketplace matching, routing, etc.)\n Experience with causal inference methods applied to operational decisions (synthetic controls, geo-experiments, switchbacks)\n Experience contributing to or designing experimentation platforms, not just using them\n Exposure to AI/ML products, large language models, or large-scale inference systems\n Track record of setting technical direction across multiple workstreams or mentoring senior ICs without formal management responsibility\n \n Deadline to apply: None. Applications are accepted on a rolling basis.\n The annual compensation range for this role is listed below. \n 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.\n Annual Salary:\n $275,000 — $370,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n 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.\n 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.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualif","salary_min":275000,"salary_max":370000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["payments","llm","alignment","data-science"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5212119008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-07T18:16:13Z","expires_at":"2026-06-29T14:00:13.49099Z","created_at":"2026-05-08T14:00:13.513051Z","updated_at":"2026-05-30T14:00:13.605199Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8473e70-d698-4d8a-8e2b-7201b8cca131"},{"id":"eb1820ca-fddb-4ae1-893d-54b181894b48","company_id":"e12d7a84-7538-4599-9b03-0cce91dc76b4","title":"Distinguished Engineer, Agentic SDLC \u0026 Non‑Linear Productivity","slug":"distinguished-engineer-agentic-sdlc-nonlinear-productivity-598d5103","description":"GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.\n The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.\n * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. \n About the Role \n We are looking for a Distinguished Engineer to pioneer and scale autonomous, agentic SDLC capabilities across GitLab. Distinguished Engineers are recognized experts across multiple technology domains and represent the most senior level of technical leadership within and across divisions at the company.\n In this role you will deeply immerse in GitLab's product and internal engineering workflows to identify classes of problems that can be wholly or largely addressed by AI agents, validate them through rigorous experimentation in production-adjacent environments, and codify patterns that can be productized for our millions of users. You will act as a bridge between Architecture, Product, Infrastructure, and Data and ML teams, ensuring that agentic capabilities deliver durable value internally first and then scale reliably and securely to our customers.\n This role reports to a Director-level engineering leader.\n What You'll Do \n \n Define and continuously refine a company-wide technical vision for autonomous, agentic SDLC that aligns with GitLab's product strategy and Engineering job architecture.\n Identify and prioritize non-linear productivity opportunities across the SDLC, from planning and coding to review, security, compliance, and operations, targeting 10x step changes rather than incremental gains.\n Translate ambiguous problem spaces into concrete, iterable roadmaps in partnership with Product, AI and ML, and Architecture teams.\n Lead hands-on experiments and prototypes to validate where agentic workflows can fully own or materially reshape engineering tasks, including autonomous MR authoring, test creation and triage, security remediation, release readiness, and incident response.\n Design and implement reference architectures for agentic SDLC inside GitLab, including orchestration patterns, safety guardrails, observability, and human-in-the-loop controls.\n Define evaluation frameworks using offline benchmarks and online experiments to measure correctness, latency, safety, cost, and productivity impact of agentic workflows.\n Select and own a small set of high-impact internal use cases as pathfinders and drive them from concept through adoption to measurable productivity gains.\n Work directly with engineering teams to embed agentic workflows into day-to-day development, ensuring they are trusted, observable, and resilient.\n Define and track core productivity metrics such as cycle time, MTTR, and MR throughput, and link agentic interventions to real business outcomes.\n Capture and codify reusable patterns, libraries, and playbooks that other teams can adopt with minimal friction.\n Work with Product Management and Engineering leadership to convert proven internal patterns into product capabilities that can be safely and reliably offered to customers.\n Ensure designs respect multi-tenant, compliance, and data governance requirements across GitLab.com and self-managed customers.\n Serve as a point of escalation for complex technical and architectural decisions related to agentic workflows, AI safety, and large-scale systems integration.\n Mentor Principal and Staff Engineers working on AI and agentic efforts, raising the overall bar for technical execution, experimentation rigor, and cross-team collaboration.\n Write clear, opinionated design documents, architecture narratives, and decision records that help teams make aligned, high-quality decisions independently.\n Represent GitLab in the broader ecosystem at conferences, standards groups, and open source communities on topics such as AI-assisted development, autonomous agents, and productivity measurement.\n","salary_min":260000,"salary_max":345000,"location":"Remote (Canada)","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["llm","alignment","distributed-systems","agents"],"apply_url":"https://job-boards.greenhouse.io/gitlab/jobs/8537853002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-07T14:28:50Z","expires_at":"2026-06-29T14:08:37.890277Z","created_at":"2026-05-08T14:09:08.355776Z","updated_at":"2026-05-30T14:08:38.006407Z","company_name":"GitLab","company_slug":"gitlab","company_logo_url":"https://www.google.com/s2/favicons?domain=about.gitlab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/eb1820ca-fddb-4ae1-893d-54b181894b48"},{"id":"fad2f7a2-bd11-4215-b4fb-85ede2f803fe","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff Software Engineer, Kubernetes Platform","slug":"staff-software-engineer-kubernetes-platform-34576b6d","description":"About Anthropic \n 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.\n About the role \n Anthropic runs some of the largest Kubernetes clusters in the industry. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. The Kubernetes Platform team owns the Kubernetes control plane that makes those clusters work.\n We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself — apiserver, etcd, controllers — so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure.\n We make sure the control plane is fast, correct, and always available. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow.\n Key responsibilities \n \n Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption\n Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us\n Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on\n Build and maintain custom controllers, operators, and CRDs\n Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities\n Collaborate with cloud providers on required features and escalations\n Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures\n \n Minimum qualifications \n \n Significant software engineering experience building and operating production distributed systems\n Proficiency in at least one systems-appropriate language (e.g., Go, Python, Rust, or C++)\n Deep, hands-on Kubernetes experience (well beyond \"user of”) into scheduler, controllers, apiserver, or operating large multi-tenant clusters\n Demonstrated ability to debug complex issues across the stack, from API behavior down to node and network-level root causes\n A track record of designing for reliability, correctness, and clear failure semantics in systems other engineers depend on\n Strong written and verbal communication; comfort building consensus with internal stakeholders\n \n Preferred qualifications \n \n Experience with Kubernetes internals or contributions: kube-scheduler / scheduling framework, apiserver, etcd, client-go, controller-runtime, or similar\n Experience building or operating cluster schedulers or batch systems (e.g., Kueue, Volcano, Slurm, or in-house equivalents)\n Background scaling control planes or coordination systems (etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments)\n Familiarity with ML infrastructure: GPUs, TPUs, or Trainium; gang scheduling; topology-aware placement; collective networking such as NCCL\n Experience with GCP and/or AWS, including GKE/EKS internals and Infrastructure as Code\n Low-level systems experience such as Linux kernel tuning, cgroups, or eBPF\n 8+ years of relevant industry experience, including time leading large, ambiguous infrastructure projects\n The annual compensation range for this role is listed below. \n 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.\n Annual Salary:\n $320,000 — $405,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n 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.\n 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.\n We encourage you to apply","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["alignment","cloud","distributed-systems","infrastructure","kubernetes"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5211241008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-06T07:14:11Z","expires_at":"2026-06-29T14:00:27.46948Z","created_at":"2026-05-06T14:00:40.021287Z","updated_at":"2026-05-30T14:00:27.57948Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fad2f7a2-bd11-4215-b4fb-85ede2f803fe"}],"page":1,"per_page":20,"total":369,"total_pages":19}