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).
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 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/Research Scientist, Audio Research Engineer/Scientist focused on audio AI, working on training audio models, developing novel architectures, and optimizing inference for speech and audio understanding and generation systems. | Post-trainServe |
| 9 |
| Applied Safety Research Engineer, Safeguards Research-oriented engineer to develop methods for representative, robust, and informative AI safety evaluations. This role involves designing experiments to improve model behavior evaluation, shipping these methods into pipelines that inform model training and deployment, and directly shaping how Anthropic understands and improves model safety across misuse, prompt injection, and user well-being. The role also involves building tooling for policy experts and surfacing findings to drive upstream model improvements. | Eval GatePost-train | 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 |
| Senior Research Scientist, Reward Models Senior Research Scientist focused on reward models for LLMs, involving novel architectures, RLHF, LLM-based evaluation, and mitigating reward hacking. Aims to improve model alignment with human values and translate research into production systems. | Post-trainEval Gate | 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 |
| 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 |
| Cross-functional Prompt Engineer This role focuses on shaping and owning the behavior of Claude, Anthropic's AI model, across all products. It involves authoring system prompts, developing meta-prompts for research pipelines, leading incident response for behavioral issues, and scaling best practices. The role requires strong prompting skills, technical foundations, excellent judgment, and collaboration across research, product, and safety teams. It sits at the intersection of research and product, aiming to ensure AI systems are safe, beneficial, and aligned with human values at scale. | Post-trainAgent | 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, Model Evaluations Research Engineer focused on designing and implementing Anthropic's model evaluation platform, shaping how models are understood, measured, and improved. This role involves leading the architecture of scalable evaluation infrastructure, implementing high-throughput pipelines for production training, analyzing results to guide model development, and collaborating with research and training teams. The goal is to ensure models meet high standards for capabilities and safety before deployment, influencing training decisions and the overall model roadmap. | Eval GatePost-train | 9 |
| Research Engineer, Model Evaluations Research Engineer focused on designing and implementing Anthropic's model evaluation platform, influencing training decisions and model development. This role involves leading the architecture of scalable evaluation pipelines, analyzing results, partnering with research teams, and contributing to publications. It sits at the intersection of research and engineering, with a strong emphasis on AI safety and model capabilities. | Eval GatePost-train | 9 |
| Research Engineer, Production Model Post-Training, London Research Engineer focused on post-training of production AI models, including techniques like Constitutional AI and RLHF. The role involves implementing, scaling, and optimizing these processes, conducting research to improve model quality, and developing pipelines for fine-tuning and evaluation. Requires strong software engineering skills, experience with large-scale distributed systems, and familiarity with training/fine-tuning/evaluating LLMs. The role directly impacts the quality, safety, and capabilities of production models. | Post-trainServe | 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 |
| Research Engineer, Virtual Collaborator (Cowork) Research Engineer focused on training Claude for virtual collaborator workflows, involving RL environments, data creation, and evaluation systems for enterprise use cases. | Post-trainData | 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 |
| 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 Operations & Strategy Lead - Coding & Cybersecurity Data This role focuses on building and scaling data operations for AI models, specifically for coding and cybersecurity capabilities. The lead will partner with research teams to design and execute data strategies, manage vendors, and oversee the data pipeline from requirements to production. While not hands-on engineering, technical depth in understanding training data quality is required, with a focus on strategy and execution. | DataAgent | 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 |
| Privacy Research Engineer, Safeguards Research Engineer focused on privacy for large language models, developing and auditing privacy-preserving training algorithms and techniques, and ensuring responsible data handling. | DataPost-train | 9 |
| Research Engineer, Pretraining Scaling - London Research Engineer focused on pretraining and scaling large language models, involving performance optimization, debugging, experimental design, and ensuring reliability of production training pipelines. The role is highly operational, requiring on-call incident response during model launches, and involves building and maintaining training infrastructure and codebase capabilities. | Pretrain | 9 |
| Research Engineer / Research Scientist, Biology & Life Sciences Research Engineer/Scientist role focused on applying AI/ML to accelerate progress in life sciences. The role involves developing novel evaluation frameworks and training strategies to improve AI model performance on biological research tasks, bridging domain expertise with ML engineering. It emphasizes rigorous methods, collaboration, and safety. | Post-trainEval Gate | 9 |
| Research Engineer / Scientist, Tool Use Safety Research Engineer/Scientist focused on advancing the frontier of safe tool use in AI models, specifically addressing prompt injection, data exfiltration, adversarial attacks, and autonomous agent behavior with large tool sets. The role involves designing and implementing RL methodologies, building evaluations, and shipping research advances into production models, with a strong emphasis on safety and reliability. | AgentPost-train | 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 / Scientist, Robustness Research Engineer/Scientist focused on AI robustness and safety within the Alignment Science team. The role involves conducting critical safety research and engineering to ensure AI systems can be deployed safely, with projects spanning jailbreak robustness, automated red-teaming, monitoring techniques, and applied threat modeling. It emphasizes pragmatic approaches to AI safety challenges, understanding and steering AI behavior, and contributing to research papers and safety efforts. | Post-trainAgent | 9 |
| Research Engineer / Scientist, Tool Use Research Engineer/Scientist focused on advancing the frontier of tool use for AI agents, aiming to improve accuracy, reliability, safety, and efficiency in complex workflows. The role involves defining research agendas, designing RL methodologies, building evaluations, and shipping research advances into production models, with a strong emphasis on safety and collaboration. | AgentPost-train | 9 |
| Research Engineer, Model Performance & Quality Research Engineer focused on systematically understanding and monitoring model quality in real-time. This role involves training production models, developing monitoring systems, and creating novel evaluation methodologies, bridging research and production across the model training pipeline. | Eval GatePost-train | 9 |
| Research Engineer, Virtual Collaborator Research Engineer focused on training Claude for virtual collaborator workflows using reinforcement learning, data pipelines, and integrating real organizational data. The role involves designing RL environments, scaling data creation, integrating enterprise data, developing evaluation systems, and training Claude on document manipulation, with a focus on enterprise AI applications. | Post-trainData | 9 |
| Research Scientist / Engineer, Agentic Learning (Horizons) Research Scientist/Engineer focused on developing and implementing novel finetuning techniques for language models to improve alignment properties like moral reasoning, honesty, and character. The role involves creating and maintaining evaluation frameworks, collaborating on production model integration, and automating scaling processes. Requires strong Python skills, ML training/experimentation experience, and analytical skills for interpreting results. Experience with language model finetuning, AI alignment research, and techniques like RLHF is preferred. | Post-train | 9 |
| Research Engineer / Scientist, Model Welfare Research Engineer/Scientist focused on understanding, evaluating, and mitigating potential welfare and moral status concerns of AI systems. This involves technical research projects on model characteristics relevant to welfare, designing interventions, and collaborating with other AI safety and alignment teams. The role also involves improving and expanding welfare assessments for frontier models and potentially deploying interventions into production. | Post-trainEval Gate | 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 |
| Research Engineer, Model Performance & Quality Research Engineer focused on systematically understanding and monitoring model quality in real-time. This role involves training production models, developing monitoring systems, and creating novel evaluation methodologies, bridging research and production across the model training pipeline. | Eval GatePost-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 |
| Research Manager, Tokens Research Manager for the Pretraining Data team (Tokens) at Anthropic. Focuses on understanding and innovating pretraining data for foundational AI models, including data trends, scaling laws, data sources, and processing methodologies. Leads a team of researchers and engineers. | Pretrain | 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 |
| Research Scientist / Research Engineer, Pre-training Research Engineer role focused on the pre-training of large language models, involving research into model architecture, algorithms, data processing, and optimizer development, as well as scaling training infrastructure and developing dev tooling. Requires advanced degree, strong software engineering skills, and familiarity with large-scale ML and deep learning frameworks. | Pretrain | 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 |
| Research Engineer, Tokens ML Infra Research Engineer focused on ML training infrastructure for large language models, involving JAX/PyTorch, distributed systems, performance optimization, and MLOps tooling to support novel training architectures and experimentation. | Pretrain | 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 / Research Scientist, Multimodal Research Engineer/Scientist focused on building and studying multimodal AI systems, including training, inference, system design, and data collection. The role involves developing new architectures, reinforcement learning environments, high-performance serving infrastructure, and data processing tools for multimodal data. | PretrainPost-train | 9 |
| Research Scientist, Tokens (Multimodal) Research Scientist focused on multimodal AI systems, working on training, inference, system design, and data collection. The role involves developing new architectures for multimodal data, building infrastructure for RL environments and RPC servers, and collecting/processing large-scale multimodal data. Emphasis on foundational research and large-scale experiments. | PretrainPost-train | 9 |