Claude Jobs

Anthropic

Safeguards Labs is a new team at Anthropic focused on AI safety, operating at the intersection of research and engineering. The team's mission is to investigate novel safety methods to protect users and the AI system itself. We prototype new approaches to safe models, usage safeguards, and production safety, pressure-testing ideas through offline analysis and subsets of traffic before they are implemented in production systems. This role involves defining and executing the Labs research agenda, scoping projects, running experiments end-to-end, and deciding on the viability of research ideas. The team is structured with a high ratio of researchers to software engineers, offering substantial autonomy and influence over the team's direction.

Anthropic

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends.

Anthropic

The Cybersecurity RL team within Anthropic is seeking a Research Engineer to advance the capabilities of AI models in secure coding, vulnerability remediation, and defensive cybersecurity. This role combines research and engineering, requiring the design and implementation of RL environments, conducting experiments, delivering work into production training runs, and collaborating with cross-functional teams. The ideal candidate will have domain expertise in cybersecurity and an interest or experience in training safe AI models, potentially coming from backgrounds like white hat hacking, security engineering, or detection and response.

Anthropic

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the RL Teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators. You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will: - Invent, design and implement RL environments and evaluations. - Conduct experiments and shape our research roadmap. - Deliver your work into training runs. - Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic. You may be a good fit if you: - Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch). - Have worked across the stack – kernels, model code, distributed systems. - Know how to balance research exploration with engineering implementation. - Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: - Experience with reinforcement learning. - Experience porting ML workloads between different types of accelerators. - Familiarity with LLM training methodologies. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000—$850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Anthropic

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Anthropic

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Anthropic

Anthropic is seeking Research Engineers to reimagine how Claude interacts with external data sources. This role involves designing novel architectures for organizing information and training language models to effectively utilize these architectures. The goal is to move beyond traditional data paradigms to accommodate the capabilities of Large Language Models (LLMs).

Anthropic

The Knowledge Work team at Anthropic builds the training environments and evaluations that empower Claude to excel in professional workflows, including searching, analyzing, and creating content across various tools and documents. As this work expands, the underlying systems require the same rigor as the research itself. In this role, you will design and execute experiments to enhance Claude's ability to search, retrieve, and reason over information at scale. Your responsibilities will encompass environment design, data curation, RL training, evaluation, and supporting infrastructure, requiring flexibility to address progress blockers. You will collaborate closely with researchers and other RL teams to implement capabilities that directly influence Claude's performance. We believe that true ownership and impact stem from both hardening existing environments and creating new ones, ensuring the quality of the entire stack that drives superhuman epistemics.

Anthropic

The Interpretability team at Anthropic is dedicated to understanding how large language models work, believing that a mechanistic understanding is key to making advanced AI systems safe and reliable. This role involves building and maintaining the specialized infrastructure for interpretability research, akin to performing 'neuroscience' on neural networks. The work spans the entire lifecycle of a production language model, from pretraining and inference to performance optimization, pushing the boundaries of hardware and software to address critical bottlenecks. As interpretability research matures and is applied to safety audits on frontier models, engineering and infrastructure have become crucial, making this role directly impactful on one of AI's most significant open problems.

Anthropic

As a Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis. The Economic Research team studies the economic implications of AI on individual, firm, and economy-wide outcomes, building scalable systems to monitor AI usage patterns and measure the impact of AI adoption on real-world outcomes. You will collaborate closely with teams across Anthropic, including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy, to build scalable and robust data systems supporting high-leverage, high-impact research.

Anthropic

Anthropic is building reliable, interpretable, and steerable AI systems to be safe and beneficial for users and society. We are a growing team of researchers, engineers, policy experts, and business leaders dedicated to this mission. We are seeking a Staff Software Engineer to lead technical direction at the intersection of engineering and research for the Claude Code team. In this role, you will collaborate with researchers and engineering leadership to define how we measure, understand, and enhance Claude's coding abilities. You will architect the systems, tooling, and evaluation infrastructure that accelerate our research progress and be responsible for technical decisions impacting the team and beyond. This senior individual contributor position is for someone with a proven track record of building and owning large-scale systems, ready to take on a technical leadership role by driving architecture, mentoring engineers, and influencing the future of Claude Code.

Anthropic

As a Regional Research Economist, you will collaborate with governments, academia, industry, and civil society to measure and understand AI's economic effects and explore policy interventions. You will contribute to the Anthropic Economic Index, developing new methodologies to assess AI's usage, diffusion, and impact, and broadening access to these insights. This role involves using advanced econometrics, machine learning, and structural estimation to drive impact on policy discussions and inform Anthropic's business and product strategies. The team focuses on rigorous empirical methods and novel measurement approaches, building datasets to track AI's impact on labor markets, productivity, and economic transformation using a privacy-preserving system to analyze real-world AI interactions.

Anthropic

Anthropic is building reliable, interpretable, and steerable AI systems to be safe and beneficial for users and society. As a Product Manager focused on Developer Productivity, you will partner with Infrastructure, Inference, Research, and Product Engineering teams to develop the systems that enable thousands of engineers and researchers to build, test, and ship code. Your work will directly impact engineering velocity by defining abstractions for code development, establishing metrics to identify friction, and making trade-offs to ensure a rapidly scaling organization ships with confidence. You will drive the evolution of our developer platform as AI agents become autonomous collaborators, redefining developer productivity in an AI-native world.

Anthropic

As a Product Manager on Claude Code's model performance team, you will drive model launches end-to-end, build evals that measure what matters, and partner directly with researchers and product engineers to translate model improvements into developer-facing outcomes. Claude Code is the most capable coding agent in the world, and this role is crucial for extracting its maximum performance. You will act as the connective tissue between frontier research and the millions of developers who depend on Claude Code.

Anthropic

Anthropic's Human Data Platform team is responsible for building systems that enhance our AI models by collecting crucial data. This involves creating infrastructure for simulating real-world environments and tasks, developing innovative interfaces for data vendors, and establishing pipelines for researchers to acquire high-quality data efficiently. As the real-world application of Claude expands, so does the need for sophisticated data collection tools. This role will collaborate closely with a fast-paced engineering team, contribute to strategic decision-making, and ensure investments are made in scalable tooling. The position requires working across research teams, data operations, and external vendors to translate insights into actionable product direction.

Anthropic

Anthropic's inference fleet serves Claude to millions of users across its own products and major cloud platforms. The stack supporting this is complex, involving accelerator kernels, model servers, distributed routing, autoscaling, and capacity management, where each layer's performance impacts the others. The Inference System Dynamics team is tasked with understanding this entire system and ensuring high performance across throughput, latency, reliability, and correctness. This involves measuring fleet performance against theoretical limits, investigating performance gaps, and owning correctness checks to ensure Claude's outputs are accurate across various hardware and serving configurations. The team focuses on identifying high-leverage optimization opportunities across components and collaborating with owning teams to implement them, treating correctness as an integral part of performance.

Anthropic

Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency. Working at the intersection of hardware and software, you'll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization. Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.

Anthropic

Anthropic is building reliable, interpretable, and steerable AI systems to be safe and beneficial for users and society. This role will manage the broad sourcing operations essential for Anthropic's global data center infrastructure, encompassing compute, networking, racks, mechanical components, cabling, and the associated supplier ecosystem. The successful candidate will ensure timely delivery of hardware to the correct sites under favorable terms, acting as the operational owner for these critical infrastructure elements.

Anthropic

Anthropic's mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization. In this role, you will build and scale the critical infrastructure that powers our AI safety systems, working at the intersection of machine learning, large-scale distributed systems, and AI safety. You will develop the platforms and tools that enable our safeguards to operate reliably at scale and directly contribute to making AI systems more trustworthy and aligned with human values.

Anthropic

As part of our growing Data Science and Analytics team, you will play a key role in Anthropic's mission of building safe and beneficial AI by driving data-informed decision-making across the organization. You'll be embedded with teams supporting our Developer Platform — the infrastructure that enables developers and enterprise customers to build on Claude via our core API, agent orchestration, tool and MCP integrations, and knowledge management capabilities. In this role, you'll partner closely with product, engineering, and go-to-market teams to understand how AI agents are being built and deployed at scale. You'll identify growth opportunities, surface insights about platform adoption, and drive data-informed decisions that shape our platform roadmap. You'll also help establish the analytical foundations and best practices of a data science team that is still defining itself — bringing rigor and clarity to a fast-moving environment.