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
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 24 new AI-related roles. That is a -27% change versus the prior 30 days (33 → 24).
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.
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, RL Scaling Science Research Engineer focused on scaling Reinforcement Learning (RL) for frontier models. Designs and runs large-scale RL experiments to understand and resolve bottlenecks, builds benchmarks for long-horizon progress, and ships validated findings into production training recipes. Operates at the research/engineering boundary. | Post-trainPretrain | 10 |
| Research Engineer, Science of Scaling Research Engineer/Scientist on the Science of Scaling team, focused on developing next-generation large language models. The role involves research at the intersection of cutting-edge research and practical engineering, contributing to safe, steerable, and trustworthy AI systems. Responsibilities include research into the science of converting compute into intelligence, leading research projects, designing and analyzing experiments, optimizing training infrastructure, and developing dev tooling. Requires significant software engineering experience, proficiency in Python and deep learning frameworks, and a results-oriented approach. Strong candidates may have experience with JAX, reinforcement learning, high-performance ML systems, accelerators, Kubernetes, OS internals, transformer architectures, large-scale ETL, and distributed training at scale. |
| Pretrain |
| 10 |
| Research Engineer / Research Scientist, Pre-training Research Engineer/Scientist focused on pre-training large language models, with an emphasis on multimodal capabilities. The role involves research, implementation, experimentation, and optimization of training infrastructure and model architectures, contributing to the development of safe and steerable AI systems. | Pretrain | 10 |
| Research Engineer, Pretraining Research Engineer focused on pretraining large language models, involving research into model architecture, algorithms, data processing, and optimizers, along with scaling training infrastructure and analyzing experiments. The role contributes to the entire stack from low-level optimizations to high-level model design. | Pretrain | 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, and creating agentic models for tasks like computer use and autonomous software generation, while also improving reasoning abilities and developing prototypes. Key responsibilities include architecting RL infrastructure, designing training environments and methodologies, driving performance improvements, and collaborating across teams. | AgentPost-train | 10 |
| Research Engineer, Frontier Red Team (Autonomy) Research Engineer focused on building and evaluating autonomous AI systems and defensive agents to counter adversarial AI, with a focus on cyberphysical risks and AI safety. This role involves creating model organisms, developing defensive agents, and translating findings into policy-relevant demonstrations. | AgentEval Gate | 10 |
| 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 |
| Anthropic AI Safety Fellow, Canada This is a fellowship program focused on AI safety research, aiming to bridge industry engineering expertise with research skills. Fellows will work on empirical projects using external infrastructure, with the goal of producing public outputs like paper submissions. The program offers mentorship, funding, and compute resources. | Post-train | 10 |
| Anthropic AI Safety Fellow, UK This is a research fellowship focused on AI safety, aiming to produce empirical research outputs like paper submissions. Fellows will use external infrastructure and work on projects aligned with Anthropic's research priorities, receiving mentorship and resources. | Pretrain | 10 |
| Anthropic AI Safety Fellow, US This is a fellowship program focused on AI safety research, aiming to bridge industry engineering expertise with research skills. Fellows will work on empirical projects using external infrastructure, with the goal of producing public outputs like paper submissions. The program offers mentorship, funding, and compute resources. | Post-train | 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 |
| Research Engineer / Research Scientist, Pre-training Research Engineer/Scientist focused on pre-training large language models, with an emphasis on multimodal capabilities. The role involves research, implementation, experiment design, and optimizing training infrastructure for next-generation AI systems. | 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 |
| Research Scientist / Research Engineer, Pre-training Research Engineer role focused on pre-training large language models, involving research into model architecture, algorithms, data processing, and optimizer development, alongside optimizing and scaling training infrastructure. Requires advanced degree, strong software engineering skills, and expertise in Python and deep learning frameworks. | Pretrain | 10 |
| Research Engineer, Interpretability Research Engineer focused on mechanistic interpretability to understand and improve the safety of large language models. This involves implementing and analyzing experiments, optimizing research workflows, building tools for experimentation, and developing infrastructure to support model safety improvements. | Post-train | 10 |
| Research Manager, Interpretability Manager for the Interpretability team focused on mechanistic interpretability of large language models, aiming to understand how they work internally for AI safety. | Post-train | 10 |
| Research Scientist, Interpretability Research Scientist focused on mechanistic interpretability of LLMs, aiming to understand how neural network parameters map to algorithms for safety and steerability. Involves developing methods, running experiments, building infrastructure, and communicating results. | Post-train | 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, Discovery Technical Program Manager on the Discovery team, owning systems and programs that determine research velocity, including compute planning, scientific RL environment health, and vendor pipelines. Requires ML engineering or research background with program leadership experience, technical depth to debug pipelines and analyze RL transcripts, and organizational effectiveness to coordinate across research, infrastructure, product, and data operations. | Post-trainData | 9 |
| Research Engineer, Search and Knowledge Post-Training Research Engineer focused on advancing search and knowledge capabilities in LLMs through post-training techniques. The role involves defining research hypotheses, designing experiments, building instrumentation for controlled studies, developing evaluations to distinguish reasoning from pattern matching, and driving optimization rigor. It sits at the intersection of RL, retrieval, and evaluation, aiming to make LLMs trustworthy searchers. | 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, Model Evaluations Research Engineer focused on building and operating the evaluation infrastructure for large language models, ensuring their capabilities, knowledge, and safety properties are rigorously measured and validated at scale. This role involves designing evaluations, building distributed systems for running them, monitoring model health during training, and partnering with researchers to interpret results. | Eval GatePost-train | 9 |
| Research Engineer, RL Infrastructure (Knowledge Work) Research Engineer focused on the reliability, observability, and infrastructure of training environments and evaluation systems for AI models, ensuring stability and quality as they scale. The role involves proactive hardening, building tooling for early problem detection, and serving as a dedicated owner for environment health and evaluation integrity. | Eval GateData | 9 |
| Research Engineer, Machine Learning (RL Velocity) The RL Velocity team owns the efficiency and reliability of the RL Science stack, building and improving the core platform for RL training runs to remove bottlenecks and enable faster iteration. This role focuses on ML infrastructure, distributed systems, and research tooling to improve the velocity and reliability of RL training at scale. | DataPost-train | 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 |