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 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.
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).
| Title | Stage | AI score |
|---|---|---|
| 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 |
| 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 |
| 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 |
| [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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Manager of Forward Deployed Engineering Manager of Forward Deployed Engineering at Anthropic, responsible for leading a team that embeds with strategic customers to ship production AI applications and agent deployments built on Claude. This player-coach role involves hiring, developing, and mentoring engineers, overseeing customer engagements, reviewing technical architectures, and collaborating with cross-functional teams to drive AI transformation for enterprise clients. | Agent | 9 |
| Security Labs Engineer This role focuses on executing security R&D projects end-to-end, building novel security infrastructure, and driving successful experiments toward production scale. It involves working with research teams to test security controls, evaluating new security technologies, and documenting results to inform future security architecture. The role spans from initial project scoping to potential production deployment, with a focus on high-assurance environments and AI-assisted security tooling. | ServeShip | 9 |
| Forward Deployed Engineer The Forward Deployed Engineer (FDE) role at Anthropic focuses on embedding with strategic customers to drive the adoption of advanced AI applications. This role involves building production applications using Claude models within customer systems, delivering technical artifacts like sub-agents and agent skills, and providing deployment support. The FDE will work closely with post-sales, product, and engineering teams, combining engineering expertise with customer-facing skills to solve complex business challenges and represent Anthropic's mission. | Agent | 9 |
| Prompt Engineer, Agent Prompts & Evals This role focuses on prompt engineering and evaluation development for AI-first products and features, bridging model capabilities with user experience. It involves designing, testing, and optimizing prompts, building evaluation suites, supporting model launches, and contributing to prompt development frameworks. The role requires strong software engineering skills, LLM and prompt engineering experience, and understanding of evaluation methodologies. | AgentEval Gate | 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 |
| Applied AI Engineer, Startups Applied AI Engineer role focused on advising and partnering with AI-native startups to build on the Claude Developer Platform. Responsibilities include technical guidance, developing evaluation frameworks, designing scalable architectures, and creating technical resources to help startups succeed with Claude. Requires production experience with LLM-powered applications, agent architectures, and evaluation frameworks. | Agent | 9 |
| Research Engineer, Reward Models Platform Research Engineer focused on building platforms and infrastructure to automate and accelerate the reward model development and evaluation workflows for ML researchers at Anthropic. The role involves creating tools for rubric development, human feedback analysis, reward robustness evaluation, and detecting reward hacks, with the goal of enabling rapid iteration and improving reward signal quality for training AI models. | Post-train | 9 |
| Forward Deployed Engineer Forward Deployed Engineer (FDE) embeds with strategic customers to drive AI adoption by shipping advanced AI applications built on Claude models. Collaborates with customer teams, Post-Sales, Product, and Engineering to solve business challenges using frontier AI, focusing on safety and reliability. Operates autonomously, builds customer relationships, and identifies new AI deployment opportunities. | Agent | 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 |
| Staff Infrastructure Engineer, Pre-training Staff Infrastructure Engineer focused on the data processing infrastructure for large language model pre-training. This role involves designing, implementing, and optimizing scalable systems for data quality, validation, and distributed computing at web-scale, collaborating closely with research teams. | Data | 9 |
| Machine Learning Systems Engineer, RL Engineering ML Systems Engineer focused on Reinforcement Learning Engineering to build, maintain, and improve the algorithms and infrastructure for training AI models like Claude using RLHF and other advanced techniques. The role emphasizes improving system performance, robustness, and usability to accelerate research breakthroughs in AI capabilities and safety. | Post-train | 9 |
| Machine Learning Systems Engineer, Research Tools Machine Learning Systems Engineer focused on developing and optimizing encodings and tokenization systems for Anthropic's Finetuning workflows. This role acts as a bridge between Pretraining and Finetuning teams, building infrastructure crucial for model learning and data interpretation, impacting research progress and efficiency. | DataPost-train | 9 |
| Data Operations Manager - Computer Use & Tool Use This role focuses on building and scaling data operations for AI models, specifically for computer use capabilities and tool use safety. The manager will partner with research teams to design and execute data strategies, manage vendors, and own the data pipeline from requirements to production. The goal is to ensure AI models can use tools safely and operate computers autonomously, impacting agentic workflows. The role requires technical depth in ML workflows and RL environments, strategic thinking, and operational excellence. | DataAgent | 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 |
| Research Engineer, CLIO Machine Learning Systems Engineer to join the Encodings and Tokenization team, focusing on developing and optimizing tokenization systems for Pretraining and Finetuning workflows. This role builds infrastructure impacting model learning and data interpretation, bridging Pretraining and Finetuning teams. | DataPost-train | 9 |
| Machine Learning Systems Engineer, Encodings and Tokenization Machine Learning Systems Engineer focused on developing and optimizing encodings and tokenization systems for Anthropic's Finetuning workflows. This role acts as a bridge between Pretraining and Finetuning teams, building infrastructure that impacts how models learn from data and improving training efficiency. Requires strong software engineering and ML expertise, with experience in ML systems, data pipelines, or ML infrastructure. | DataPost-train | 9 |
| ML Infrastructure Engineer, Safeguards ML Infrastructure Engineer focused on building and scaling critical infrastructure for AI safety systems, including real-time and batch classifier/safety evaluations, monitoring, and optimizing inference for safety-critical applications. | Eval GateServe | 9 |
| Data Operations Manager, Knowledge Lead human data collection initiatives to power advanced AI capabilities, focusing on AI safety and capability research. Design and build novel data collection systems and evaluation frameworks, translating research into scalable data systems. This is a 0-to-1 role requiring operational excellence at the intersection of AI research and execution. | DataEval Gate | 9 |
| Data Operations Manager, Horizons This role leads human data collection initiatives to power advanced AI research, focusing on agentic AI systems, coding, and computer use capabilities. It involves designing and building scalable data collection methodologies and systems from scratch, acting as a 'data as the product' owner for critical AI research. The role requires a strong software engineering background with entrepreneurial experience, technical depth in ML workflows, and project management skills. | DataAgent | 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 |
| Engineering Manager, ML Performance and Scaling Engineering Manager for ML Performance and Scaling teams, focusing on optimizing inference and training systems, identifying bottlenecks, and maximizing efficiency. Requires management experience, background in ML/AI, and interest in safe AI development. | ServePost-train | 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 |
| Machine Learning Engineer, Safeguards Research Machine Learning Engineer focused on safeguards research, bridging research and engineering. This role involves developing end-to-end pipelines and ML systems for safety research, including training/fine-tuning models, building scalable infrastructure for evaluation, implementing efficient training pipelines, and creating automated systems to understand and mitigate AI risks. The role requires strong ML fundamentals, engineering practices, and experience with Python, ML frameworks, and LLMs. | Post-trainServe | 9 |
| Machine Learning Systems Engineer - Infrastructure & Runtime, Horizons Machine Learning Systems Engineer focused on building and maintaining foundational infrastructure for AI research, specifically for reinforcement learning, agentic AI, and model evaluation. The role involves designing data pipelines, creating secure execution environments, optimizing distributed computing infrastructure, and translating research requirements into scalable systems. | DataAgent | 9 |
| Machine Learning Systems Engineer - Data & Evaluation, Horizons Machine Learning Systems Engineer on the Horizons team, focusing on building software infrastructure for AI models to use tools effectively and measure performance. This involves extending the agent framework, creating evaluations, managing training data pipelines, and applying data science techniques to improve model capabilities. The role combines software development with empirical analysis to advance model performance and capabilities, working closely with research and production teams. | AgentEval Gate | 9 |
| TPU Kernel Engineer This role focuses on optimizing ML systems, particularly for TPUs, by designing and implementing kernels to improve performance for research, training, and inference. It involves low-level optimization and providing feedback on model performance impacts. | ServePost-train | 9 |
| Machine Learning Systems Engineer, Encodings and Tokenization Machine Learning Systems Engineer focused on developing and optimizing encodings and tokenization systems for Anthropic's Finetuning workflows, acting as a bridge between Pretraining and Finetuning teams. This role is crucial for improving model training efficiency and performance, enabling researchers to experiment with new tokenization methods, and ensuring the reliability and interpretability of AI systems. | DataPost-train | 9 |
| Machine Learning Systems Engineer, RL Engineering This role focuses on building, maintaining, and improving the critical algorithms and infrastructure for training AI models, specifically using RLHF and other advanced techniques. The goal is to enhance the performance, robustness, speed, reliability, and usability of these training systems to enable breakthroughs in AI capabilities and safety. | Post-train | 9 |
| Team Manager, Alignment RL Manager for a team developing and implementing AI alignment techniques, focusing on improving model values and behavior for hard-to-evaluate tasks. The role involves driving execution of alignment initiatives, supporting team growth, and ensuring collaboration across research. Key activities include implementing and scaling techniques like oversight, synthetic data generation, and training models to assist in model training, aiming to accelerate the deployment of alignment advances into frontier models. | Post-trainData | 9 |
| Staff Software Engineer, Claude Code Software Engineer to build and maintain new agentic coding tools for developers, leveraging advanced LLM features like tool-use, chaining, and orchestration. Requires expertise in React, full-stack development, and hands-on experience with LLMs and prompt engineering. Experience with safety, security, or compliance requirements is a plus. | Agent | 8 |
| Applied AI Engineer, Beneficial Deployments Applied AI Engineer focused on deploying AI for social impact partners, advising on AI systems, building ecosystem tooling, and prototyping agents. Requires production experience with LLM applications and a builder mindset. | Agent | 8 |
| Red Team Engineer, Safeguards This role focuses on adversarial testing and red teaming of AI systems and products to uncover vulnerabilities and ensure safety. It involves simulating sophisticated threat actors, researching novel testing approaches for capabilities like agent systems and tool use, and developing automated testing frameworks. The goal is to translate findings into concrete improvements and establish metrics for detection effectiveness. | Agent | 8 |
| Engineering Manager, Safeguards Interventions Engineering Manager for Anthropic's Safeguards Interventions team, responsible for leading a team that develops and deploys systems to handle safety violations in AI models. The role involves roadmap ownership, cross-functional collaboration, ensuring production reliability, and making critical safety vs. product tradeoff decisions for AI products. | Ship | 8 |
| Engineering Manager, Agent Runtime Platform Engineering Manager for the Agent Runtime Platform, responsible for provisioning and running compute and runtimes for internal agents. This role involves owning technical strategy, managing execution, prioritizing work, building and shipping the platform, defining secure agent execution, building reusable primitives, and ensuring infrastructure scalability and reliability. The role also requires collaboration with security teams and supporting internal teams in agent development. | AgentServe | 8 |
| Staff Software Engineer, Labs: Applied AI Staff Software Engineer, Applied AI role at Anthropic focused on building full-stack applications that bring frontier AI capabilities into workflows for non-technical professionals in less software-native roles. The role involves rapid prototyping, user immersion in unfamiliar domains, collaboration with research teams, and rigorous experimentation to validate product concepts, aiming to ship early and often to maximize learning in an ambiguous, uncharted territory. | Ship | 8 |
| Engineering Manager, Agent Runtime Platform Engineering Manager for the Agent Runtime Platform, responsible for provisioning and running compute and runtimes for internal agents. The role involves owning technical strategy, managing execution, prioritizing work, building and shipping the runtime platform and tooling, defining secure agent execution, building reusable primitives, and ensuring infrastructure scalability and reliability. Requires strong experience in building and operating large-scale platforms/distributed systems and technical management, with a focus on agent platforms and security-sensitive infrastructure. | AgentServe | 8 |
| Staff+ Software Engineer, Vertical AI Products (Multiple Roles) Staff+ Software Engineer for Anthropic's Vertical AI Products team, focusing on building industry-specific AI products (financial services, science, healthcare, enterprise) that integrate Claude. The role involves technical leadership, end-to-end product ownership, close collaboration with research, and direct customer engagement to shape and deliver AI solutions. | Ship | 8 |
| Manager, Applied AI Engineering Manager of Applied AI Engineers leading a team that advises enterprise customers on adopting and deploying LLM APIs (Claude). Responsibilities include hiring, coaching, setting technical direction, developing evaluation frameworks, and channeling field insights back into product development. Focuses on advanced implementation patterns for LLMs, including prompting and agentic systems. | Agent | 8 |
| Engineering Manager, Cloud Safety Engineering Manager to lead the Cloud Safety team, responsible for scaling and optimizing Claude's serving infrastructure across Cloud Service Providers (CSPs). The role involves owning end-to-end safety, including API, inference, classifiers, fraud detection, data management, and operations, to ensure safe usage and enable the launch of new models and features at scale. | Serve | 8 |
| Applied AI Engineer Applied AI Engineer role focused on being a technical advisor to customers deploying Claude (LLM). Responsibilities include guiding architecture, developing evaluation frameworks, and implementing cutting-edge LLM patterns via API. Requires strong Python skills and production experience with LLMs, including agent development and retrieval frameworks. | Agent | 8 |