Anthropic
AI Frontier · AI lab
- HQ
- San Francisco, US
- Founded
- 2021
- Size
- 1,500+
- Website
- anthropic.com
- Blog
- anthropic.com
Products
AI Frontier · AI lab
Anthropic currently has 151 active AI-related job listings, with a significant focus on roles related to agents, which constitute 32% of their openings. Engineering is the most frequent function, followed by Research. The majority of their hiring is concentrated in the United States. Frequent technical tags include evals, model_serving, and agent_orchestration, suggesting a focus on the practical deployment and management of AI systems.
Currently tracking 127 active AI roles, down 32% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $46k–$850k (avg $406k).
Anthropic currently has 149 active AI-related roles in our index. The most common open titles are: Regional Research Economist, Economic Research (2), Research Engineer, Machine Learning (RL Velocity) (2), Research Engineer, Production Model Post-Training (2), Staff Software Engineer, AI Reliability Engineering (2), Product Manager, Safeguards Rare Harms. Most positions are in Engineering and Research.
Anthropic's active AI hiring is concentrated in: agents (31%), serving infrastructure (15%), post-training (15%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Anthropic is hiring AI talent in: United States (130 roles), United Kingdom (18 roles), Canada (6 roles), Ireland (3 roles).
Job postings at Anthropic most frequently mention: Machine Learning, AI Safety, Production ML Systems, System Design, Large Language Models (LLMs).
In the past 30 days, Anthropic has posted 32 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Research Scientist, Interpretability Research Scientist focused on mechanistic interpretability of LLMs, aiming to understand how trained models work by reverse-engineering their parameters and algorithms. The role involves developing methods, designing experiments, creating interpretability features, building infrastructure, and collaborating with other teams. Requires strong scientific research background with some interpretability work, comfort with experimental science, and proficiency in Python. | Post-train | 10 |
| [Expression of Interest] Research Manager, Interpretability Research Manager for the Interpretability team, focusing on mechanistic interpretability to understand how large language models work internally and ensure AI safety. The role involves partnering with a research lead on direction, project planning, execution, hiring, and people development, translating research ideas into tangible goals, and overseeing their execution. This is a management role, distinct from individual contributor research scientist or engineer roles. |
| Post-train |
| 10 |
| Research Engineer, Pretraining Scaling Research Engineer focused on training production pretrained models at scale, involving performance optimization, debugging, experimental design, and incident response during model launches. The role bridges research and engineering, working across the full training stack. | Pretrain | 10 |
| Research Engineer/Research Scientist, Pre-training Research Engineer/Scientist focused on pre-training large language models, involving research in model architecture, algorithms, data processing, and optimizer development, as well as optimizing and scaling training infrastructure. | Pretrain | 10 |
| Staff Research Engineer, Discovery Team Staff Research Engineer focused on building AI systems capable of scientific discovery and long-horizon reasoning, working across the full model stack from training to inference and agentic systems. | PretrainAgent | 10 |
| Research Engineer, Machine Learning (Reinforcement Learning) Research Engineer focused on Reinforcement Learning to advance capabilities and safety of large language models. This role involves implementing novel approaches, contributing to research direction, creating agentic models via tool use for tasks like computer use and autonomous software generation, and improving reasoning abilities. Projects include architecting RL infrastructure, designing training environments and evaluations for RL agents, driving performance improvements, and developing automated testing frameworks. | Post-trainAgent | 10 |
| Technical Program Manager, RL Research Technical Program Manager supporting Reinforcement Learning research, focusing on managing the research lifecycle from pre-training through post-training. The role involves tracking experiments, driving research reviews, establishing processes, and collaborating with various teams to identify blockers and make technical trade-off decisions. Requires a background in ML engineering or research, with hands-on experience in ML training pipelines, RLHF systems, and large-scale data infrastructure. | Post-trainData | 9 |
| Researcher, Cybersecurity Products Researcher on a product team focused on AI for cybersecurity, identifying, measuring, and operationalizing security capabilities in frontier models for non-expert customers. Involves rapid prototyping, rigorous evaluation, and collaboration with engineers to build usable tools. | Eval GateAgent | 9 |
| Staff Software Engineer, Code RL Staff Software Engineer focused on the engineering aspects of reinforcement learning for AI coding capabilities, specifically creating and scaling agentic coding environments. The role involves designing frameworks and APIs for researchers, managing production RL runs, and improving the reliability and structure of research codebases. It emphasizes Python expertise, API design, and anticipating system failures. | AgentData | 9 |
| Research Scientist, Takeoff Intel Research Scientist focused on measuring and understanding recursive-self-improvement in AI systems. This role involves designing evaluations, building quantitative models of capability growth, running experiments, and assessing AI R&D acceleration. The output is focused on graded assessments and system-card sections rather than traditional publications. | Eval GatePost-train | 9 |
| Staff+ Software Engineer, Claude Science Staff+ Software Engineer for Claude Science at Anthropic. This role involves building AI products that serve as a workbench for researchers, enabling them to conduct scientific work from hypothesis to publication. The engineer will partner with research teams to push model capabilities into production, shape product roadmaps, and translate user needs into engineering priorities. The role focuses on shipping AI-powered scientific tools and improving model performance for scientific tasks. | ShipAgent | 9 |
| Staff+ Software Engineer, Enterprise AI Products Staff+ Software Engineer for Anthropic's Enterprise AI Products team, focusing on building organizational context and workflows (plugins, skills, connectors, webhook-triggered agents) to make Claude a daily-use tool for enterprise customers. This role involves technical leadership, end-to-end product delivery, customer interaction, and close collaboration with research to integrate model capabilities into production. | AgentShip | 9 |
| Evals Infrastructure Tech Lead / Manager Lead the team building and scaling the distributed systems that orchestrate, schedule, and execute evals for frontier models, ensuring measurement quality, reproducibility, and that eval signal reaches decision-makers. This role involves managing engineers and contributing directly as an engineer, focusing on inference, research, and infrastructure engineering. | Eval GateServe | 9 |
| [Pipeline] Product Manager, Research (Code) Product Manager for Anthropic's Research team, focusing on ideation and deployment of new AI models and products. This role bridges frontier applied research with customer needs to create new product categories, working closely with research and engineering teams to ship model capabilities and identify transformative product opportunities. | ShipPretrain | 9 |
| Machine Learning Infrastructure Engineer, Safeguards Research Machine Learning Infrastructure Engineer for Safeguards Research at Anthropic. Focuses on building and scaling infrastructure, data pipelines, and tooling for ML research, specifically for detection and mitigation of AI misuse. Owns training, evaluation, and scoring workflows, aiming to improve iteration speed, throughput, cost, and reliability of inference and scoring workloads. Bridges research and production by creating reliable, production-grade jobs from research workflows. | ServePost-train | 9 |
| Research Scientist, Life Sciences (Computational) Research Scientist role focused on computational biology, combining deep expertise with frontier AI capabilities to accelerate scientific discovery. Responsibilities include building and maintaining analysis pipelines for large-scale biological data, designing experiments, generating hypotheses, standing up computational infrastructure, and heavily using LLMs and agent frameworks. The role aims to establish how computational biology operates at Anthropic and guide the development of AI systems for biological research. | DataAgent | 9 |
| Lead, Frontier Red Team (Cyber) Lead a new team focused on researching the impacts of advanced AI models on cybersecurity, developing defenses, and steering the world through the next generation of cybersecurity. This role involves leading a research team, publishing frontier research, and building/deploying defenses to give defenders a permanent advantage. | AgentPost-train | 9 |
| Research Engineer, Chip Design RL (Reinforcement Learning) Research Engineer role focused on applying Reinforcement Learning to chip design, specifically for agentic RTL generation, design verification, and physical design optimization. The role involves inventing RL environments, optimizing EDA tools, conducting experiments, and delivering work into research and production training runs. Requires expertise in ASIC/FPGA design and familiarity with EDA tools, with strong candidates having RL experience. | AgentData | 9 |
| Research Engineer, Life Sciences Research Engineer role focused on developing novel evaluation frameworks and training strategies for AI in life sciences, aiming to accelerate progress in biological discovery and translation. The role involves measuring and improving model performance on complex scientific tasks, with a focus on safety and beneficial impact. | Post-trainEval Gate | 9 |
| [Pipeline] Staff+ Software Engineer, Developer Acceleration Staff+ Software Engineer to join Developer Acceleration team, responsible for the infrastructure enabling thousands of employees to be productive via agents. The role will define the future of agentic productivity at scale, own technical strategy, build and ship the runtime platform and tooling, write evals to benchmark agent behaviors, and ensure infrastructure scalability and reliability. | Agent | 9 |
| Research Engineer, Computer Use Research Engineer focused on teaching AI models (Claude) to perceive, use, and understand computer interfaces, enabling them to reliably and safely operate real software. This involves designing experiments, developing evaluation frameworks, building RL training environments, and collaborating with training and product teams to integrate research advances into production. | AgentPost-train | 9 |
| Manager, Applied AI Engineering, Life Sciences (Beneficial Deployments) Manager for Applied AI Engineering focused on Life Sciences, leading a team to build and deploy AI solutions (agents, integrations, tools) for scientific organizations. The role involves deep customer engagement, understanding scientific workflows, and ensuring reliable, reproducible access to biological data for AI agents, with a strong emphasis on safety and responsible deployment in a regulated-like environment. | Agent | 9 |
| Research Engineer, Domain Scaling Research Engineer focused on scaling AI models for real-world knowledge work in domains like finance, healthcare, and legal. This role involves owning the end-to-end data strategy, from sourcing tasks to RL training, including designing reward signals, managing external data vendors, and developing QA frameworks to ensure environment quality and prevent reward hacking. It combines applied research with hands-on data work. | DataPost-train | 9 |
| Staff+ Software Engineer, Inference Runtime Staff+ Software Engineer for Anthropic's Inference Runtime team, focusing on the accelerator-agnostic core of their AI inference serving stack. The role involves setting technical direction, owning the architecture and roadmap, hands-on coding in Rust/Python, optimizing accelerator usage, and building validation systems. Requires deep systems engineering or ML infrastructure background with experience in performance optimization and large-scale distributed systems. | Serve | 9 |
| Research Engineer, Code RL (Reinforcement Learning) Research Engineer focused on Reinforcement Learning for code generation, aiming to improve models' ability to write, edit, test, debug, and ship software. This role involves designing RL environments, building reward signals, running training experiments, and improving pipeline efficiency, blending research with engineering implementation. | Post-trainAgent | 9 |
| Software Engineer, Safeguards Evals Software Engineer role focused on building and owning the evaluation infrastructure for an agentic investigation system. This involves designing experiments, constructing high-quality eval datasets, measuring agent performance, analyzing coverage gaps, and productionizing research into release pipelines. The role also involves building tooling for policy experts and constructing RL environments to improve safety investigation capabilities. | AgentEval Gate | 9 |
| Product Manager, Claude Code Model Performance Product Manager for Anthropic's Claude Code Model Performance team, responsible for driving model launches, building agentic evals, and translating research improvements into developer-facing outcomes. Requires experience building agentic evals, a systems thinking approach, and comfort with both research and engineering. | AgentEval Gate | 9 |
| Research Scientist, Life Sciences Research Scientist role focused on improving AI model capabilities for life sciences tasks. This involves building agentic tools, designing evaluation benchmarks, and applying post-training techniques to enhance model performance on scientific workflows like bioinformatics, database queries, and literature synthesis. The role bridges ML, software engineering, and biology to make AI a better research assistant in life sciences. | Post-trainAgent | 9 |
| Technical Program Manager, Research This role is a Technical Program Manager for Anthropic's research organization. The TPM will define and build programs for research teams, focusing on areas like compute, evals, and RL environments. They will drive end-to-end execution of complex research initiatives, establish processes, and ensure operational health of RL environments. The role requires a background in ML research or engineering, experience building technical programs from scratch, and the ability to navigate ambiguity in fast-moving research environments. | Post-trainData | 9 |
| Research Engineer, Machine Learning (RL Velocity) Research Engineer focused on building and improving the RL training infrastructure and tooling at Anthropic. The role involves identifying and removing bottlenecks in the RL stack, partnering with researchers and other engineering teams, and owning the reliability and performance of research runs to enable faster iteration and shipping of better models at scale. | DataPost-train | 9 |
| Research Engineer, Safeguards Labs Research Engineer focused on AI safety, investigating novel methods for detecting misuse, strengthening model safeguards, and building evaluation methodologies for AI systems, particularly in agentic workflows. The role involves leading research projects, designing offline analyses, developing prototypes, and collaborating with production teams. | Eval GatePost-train | 9 |
| Anthropic Fellows Program — Reinforcement Learning This is a research fellowship program focused on Reinforcement Learning (RL) within AI safety. Fellows will work on empirical projects, potentially using external infrastructure, with the goal of producing public outputs like paper submissions. The program emphasizes mentorship from Anthropic researchers and provides a stipend and compute funding. Key activities include building model-based tools for data quality, understanding generalization, and creating RL environments for capabilities and safety tasks. | Post-train | 9 |
| Anthropic Fellows Program — AI Safety This is a research fellowship program focused on AI safety, aiming to foster talent in empirical AI research. Fellows will work on projects aligned with Anthropic's research priorities, using external infrastructure and external models, with the goal of producing public outputs like paper submissions. Key research areas include Scalable Oversight, Adversarial Robustness and AI Control, Model Organisms, Model Internals / Mechanistic Interpretability, and AI Welfare. | Post-train | 9 |
| Research Engineer, Performance RL Research Engineer focused on Reinforcement Learning for code generation and accelerator performance, aiming to improve model reasoning and coding capabilities. The role involves inventing RL environments, conducting experiments, shaping research roadmaps, and delivering work into training runs, with a strong emphasis on collaboration and scaling research innovations. | Post-trainData | 9 |
| Product Management, Research Product Manager for Anthropic's Research team, focusing on ideation and deployment of new models and products. This role bridges applied research with customer needs, identifying and productizing frontier AI capabilities into new product categories. Requires a strong technical background in AI/ML and experience launching and scaling products. | Post-trainPretrain | 9 |
| Model Quality Software Engineer, Claude Code Staff Software Engineer to set technical direction at the intersection of engineering and research on the Claude Code team. Architect systems, tooling, and evaluation infrastructure to measure, understand, and improve Claude's coding capabilities. Drive architecture, mentor engineers, and influence the direction of Claude Code. | Eval GateAgent | 9 |
| Research Product Manager, Labs This role is for a Research Product Manager in Anthropic's Labs team, focusing on 0-to-1 product development. The individual will bridge pure research and product experimentation, identifying and defining new product categories enabled by AI. Responsibilities include understanding emerging research, identifying capabilities for new products, building prototypes, leading product strategy and execution for experimental initiatives, and validating product-market fit. The role requires a deep technical background, experience launching new products, and the ability to navigate ambiguity. | ShipPretrain | 9 |
| Research Engineer / Scientist, Frontier Red Team (Cyber) Research Engineer/Scientist focused on AI-enabled cybersecurity, developing tools and frameworks for autonomous vulnerability discovery, remediation, malware detection, and pentesting. Designs and runs experiments to evaluate AI cyber capabilities and builds infrastructure for AI systems operating in security environments. Translates findings into demonstrations for policymakers and collaborates with external experts. Senior candidates will set research strategy and own the technical roadmap. | AgentEval Gate | 9 |
| Research Engineer / Research Scientist, Vision Research Engineer/Scientist focused on vision and spatial reasoning for LLMs, working on pretraining, RL, and runtime techniques like agentic harnesses. Involves developing and evaluating multimodal capabilities, creating benchmarks, and partnering with product teams to improve Claude models. | Post-trainAgent | 9 |
| Research Engineer, Universes Research Engineer role focused on building next-generation agentic environments for training AI models. This role involves implementing novel approaches, contributing to research direction, designing training environments and methodologies, and building evaluations for capable and safe agentic AI. It blends research and engineering, with a focus on reinforcement learning and complex, long-horizon agentic tasks. | AgentPost-train | 9 |
| Anthropic Fellows Program — AI Security This is a research fellowship program focused on AI safety and security, aiming to produce public outputs like paper submissions. Fellows will use external infrastructure and open-source models, working on empirical projects with mentorship from Anthropic researchers. | Post-train | 9 |
| Anthropic Fellows Program Anthropic's Fellows Program offers a 4-month full-time research opportunity focused on AI safety and related areas. Fellows will use external infrastructure and open-source models to conduct empirical projects, aiming for public outputs like paper submissions, with mentorship from Anthropic researchers. The program is designed to foster AI research and engineering talent, regardless of previous experience, and emphasizes safety, interpretability, and steerability of AI systems. | Pretrain | 9 |
| Research Engineer, Cybersecurity Reinforcement Learning Research Engineer role focused on applying reinforcement learning to cybersecurity tasks like secure coding and vulnerability remediation, blending research and engineering to train safe AI models. Requires cybersecurity domain expertise and ML/software engineering skills. | Post-trainData | 9 |
| Research Engineer, Interpretability Research Engineer focused on building and maintaining specialized infrastructure for interpretability research in AI systems. This involves developing tools for model analysis, optimizing training and inference pipelines, and ensuring reliability for safety audits, with a strong emphasis on understanding and controlling model behavior. | Post-trainServe | 9 |
| Research Engineer / Research Scientist, Tokens Research Engineer/Scientist role focused on building large-scale ML systems, touching all parts of code and infrastructure, from cluster reliability and job efficiency to running scientific experiments and improving dev tooling. The role involves optimizing ML systems, comparing model variants, scaling training jobs, and designing fault tolerance strategies, with a focus on safe, steerable, and trustworthy AI. | PretrainServe | 9 |
| ML/Research Engineer, Safeguards ML/Research Engineer focused on detecting and mitigating misuse of AI systems, building classifiers, monitoring for harms, evaluating agentic product safety, and conducting research on red-teaming and adversarial robustness. | AgentData | 9 |
| Performance Engineer, GPU This role focuses on optimizing GPU performance and systems engineering for large language models, specifically improving utilization and efficiency for inference and training at scale. It involves deep work in GPU programming, custom kernel development, and distributed systems. | ServePretrain | 9 |
| Engineering Manager, GPU (ML Accelerator) Engineering Manager for Anthropic's performance and scaling teams, focusing on optimizing compute resources for inference and training systems. The role involves leadership, technical contribution, bottleneck identification, and ensuring efficiency in large-scale ML systems, with a strong emphasis on GPU/accelerator programming and ML/OS internals. | ServeData | 9 |
| TPU Kernel Engineer TPU Kernel Engineer responsible for identifying and addressing performance issues across ML systems (research, training, inference), with a focus on designing and optimizing kernels for TPUs. Provides feedback to researchers on model performance impact. | ServePost-train | 9 |
| Research Engineer, Discovery Research Engineer focused on building and optimizing infrastructure for AI scientist training, evaluation, and inference. The role involves identifying and resolving infra blockers, developing evaluation frameworks, managing data pipelines, and optimizing training/inference for reinforcement learning in distributed environments. | ServeData | 9 |