Anthropic
AI Frontier · AI lab
- HQ
- San Francisco, US
- Founded
- 2021
- Size
- 1,500+
- Website
- anthropic.com
- Blog
- anthropic.com
Products
Currently tracking 124 active AI roles, with 106 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $46k–$850k (avg $405k).
AI Frontier · AI lab
Anthropic has 145 active AI-related job listings. The majority of these roles are focused on agents, comprising 28% of the total. Engineering is the most frequent function, with 74 listings, followed by Research with 51. The company is primarily hiring in the United States, with 118 positions, and the United Kingdom, with 22. Frequent tech tags include model_serving, evals, and agent_orchestration, suggesting a focus on deployment and evaluation of AI systems. In the last 30 days, Anthropic posted 16 new AI roles, a 47% decrease compared to the previous 30-day period.
Anthropic currently has 132 active AI-related roles in our index. The most common open titles are: Applied AI Architect, Industries (2), 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). Most positions are in Engineering and Research.
Anthropic's active AI hiring is concentrated in: agents (28%), serving infrastructure (17%), post-training (14%). 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 (106 roles), United Kingdom (20 roles), Canada (6 roles), Ireland (5 roles).
Job postings at Anthropic most frequently reference: model serving, evals, llm observability, agent orchestration, inference infra.
In the past 30 days, Anthropic has posted 29 new AI-related roles. That is a +61% change versus the prior 30 days (18 → 29).
| 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 |
| 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 |
| 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 |
| 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, 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, 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 |
| Research Lead, Training Insights Research Lead focused on developing and executing strategies for measuring and characterizing model capabilities across training and deployment. This role involves driving original research into new evaluation methodologies, leading a team, and spanning the full lifecycle of model development, from pretraining to deployment. The work includes creating long-horizon evaluations, measuring emerging capabilities, and understanding their development during RL training and post-training. The role also involves cross-organizational collaboration to map evaluation landscapes and identify gaps, shaping the evaluation narrative for model releases, and contributing to the broader research community. | Eval GatePost-train | 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, 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 / 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 |
| 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 |
| Research Engineer / Scientist, Alignment Science Research Engineer/Scientist focused on AI safety and alignment, conducting experiments to understand and steer AI behavior, with a focus on risks from powerful future systems. Involves collaboration with interpretability, fine-tuning, and red teaming teams. Explores scalable oversight, AI control, stress-testing, automated alignment research, alignment assessments, safeguards research, and model welfare. | Post-trainAgent | 9 |
| Research Engineer, Production Model Post-Training Research Engineer focused on post-training of large language models, including techniques like Constitutional AI and RLHF, to enhance model capabilities, alignment, and safety for production Claude models. Involves implementing, scaling, and optimizing these processes, conducting research for improvements, and developing evaluation tools. | Post-train | 9 |
| [Expression of Interest] Research Scientist / Engineer, Honesty Research Scientist/Engineer focused on honesty in language models, developing techniques to minimize hallucinations and enhance truthfulness. This involves data curation, classifier development, evaluation frameworks, RAG implementation, human feedback collection, prompting pipelines, RL environments, and tools for human evaluators. | Post-trainAgent | 9 |
| Research Engineer, Knowledge Team Research Engineer focused on redesigning how LLMs interact with external data sources by designing new information architectures and training models to use them. Responsibilities include implementing information architecture strategies, performing finetuning and RL, building knowledge base eval sets, and designing agentic search capabilities. Requires strong Python, ML research experience, and experience with LLMs. Experience with complex agentic systems, RAG, and distributed information retrieval is a plus. | AgentPost-train | 9 |
| Anthropic Fellows Program — ML Systems & Performance This is a research fellowship program focused on AI systems and performance, with the goal of producing public outputs like paper submissions. Fellows will work on empirical projects, potentially involving building ML systems, data pipelines, or infrastructure for accelerators, using external infrastructure and open-source models. | Data | 8 |
| Biological Safety Research Scientist Research Scientist focused on biological safety for AI systems, applying technical skills to design and develop safety systems that detect harmful behaviors and prevent misuse. This role involves designing and executing capability evaluations, collaborating on training data and safety system training, analyzing performance, and stress-testing safeguards. The goal is to ensure biological safety is embedded throughout the model development lifecycle, balancing AI's potential in life sciences with preventing misuse. | Eval GatePost-train | 8 |
| Data Scientist, Safeguards This role focuses on building and scaling a data-driven culture within an AI company, specifically for safeguards. The Data Scientist will analyze user behavior, define key metrics, identify opportunities for product improvement, design and analyze experiments, and establish data best practices to inform product and commercial strategy for safe, frontier AI deployment. | Eval Gate | 7 |
| Anthropic Fellows Program — The Anthropic Institute Fellows (Economics & Policy) This is a research fellowship program focused on empirical projects related to AI's economic and societal impacts, with the goal of producing public outputs like research papers. Fellows will use external infrastructure and work with mentors to explore areas such as AI's economic effects, labor markets, and AI-enabled cyber/bio capabilities. | Data | 7 |
| Transformative AI Research Economist, Economic Research This role focuses on building macroeconomic models of transformative AI and developing scenario-based forecasting tools. It grounds projections in microeconomic data from the Anthropic Economic Index, analyzing millions of real-world AI interactions to understand AI's impact on labor markets, productivity, and economic transformation. The role also involves contributing to AI-powered research tools for economics. | Data | 7 |
| Research Economist, Economic Research Research Economist role focused on measuring and understanding the economic impact of AI systems, developing methodologies for the Anthropic Economic Index, and using frontier econometrics and machine learning methods. The role involves analyzing AI interactions, labor market impacts, and productivity, and translating insights into policy and product recommendations. | Data | 7 |