This role involves leading a team of Applied AI Architects to drive the adoption of Anthropic's AI products across Europe's fastest-growing tech companies. Responsibilities include managing the team, setting pre-sales standards, working with high-value customers, and representing Anthropic in the European tech community.
This role involves managing a team of Technical Deployment Leads (TDLs) in the Financial Services sector, ensuring high-quality delivery of AI solutions, and building strong relationships with executive stakeholders. Responsibilities include hiring, developing team members, managing customer engagements, and setting technical and operational standards.
This role involves serving as a technical advisor to enterprise customers adopting Anthropic's Claude AI, guiding them from initial evaluation through deployment. The Applied AI Architect will design scalable integration architectures, develop evaluation frameworks, and communicate technical concepts to both engineering teams and executives. Collaboration with Sales, Product, and Engineering teams is central to ensuring successful customer outcomes while upholding safety and reliability standards.
Design and build full-stack internal tools and platforms that empower non-technical users by identifying patterns in workflows and abstracting them into reusable systems. Work closely with stakeholders to define priorities, iterate on feedback, and ship features that drive autonomy across the organization. Use Python, TypeScript, and React in a high-impact, collaborative environment focused on AI safety and reliability.
Lead and build a sales team focused on driving adoption of AI systems within mission-driven organizations across Europe, the Middle East, and Africa. The role involves securing strategic partnerships with nonprofits, foundations, educational institutions, and social enterprises, while navigating complex stakeholder ecosystems, regulatory environments, and funding models. This is a player-coach position requiring hands-on deal closing, team development, and shaping of regional sales strategy in a rapidly evolving environment.
This role focuses on designing and operating large-scale AI infrastructure, specifically managing the lifecycle of compute nodes across cloud and on-prem environments. The engineer will lead technical strategy for node provisioning, health monitoring, and automated repair systems, ensuring high reliability and efficiency across Anthropic's GPU, TPU, and Trainium fleets. Collaboration with research, inference, and cloud provider teams is central to shaping long-term infrastructure and compute strategy.
Build and maintain foundational systems for AI safety, oversight, and intervention mechanisms, focusing on detecting unwanted model behaviors and preventing misuse. Develop infrastructure for data management, evaluation systems, and tooling for human and agentic review while ensuring high operational reliability at scale. Work closely with cross-functional teams to implement robust, multi-layered defenses in a safety-first AI environment.
This role involves managing and negotiating complex commercial and procurement contracts for Anthropic's EMEA operations, with a focus on supporting AI technology transactions. The Contracts Manager will work closely with legal and business teams to streamline contract processes, implement scalable solutions, and align regional execution with global legal strategy. Key responsibilities include drafting agreements, improving contracting efficiency, and enabling cross-functional stakeholders through local rollout of policies and initiatives.
Lead a London-based engineering team building critical connectivity infrastructure for AI agents, including the MCP proxy, OAuth systems, and token management. Drive reliability, security, and scalability across distributed systems used in enterprise environments. Collaborate closely with San Francisco teams to shape the future of agent tool integration and contribute to open standards.
This role involves advancing reinforcement learning in large language models, focusing on developing agentic systems for computer use, code generation, and reasoning. The engineer will build scalable RL infrastructure, design training environments, and collaborate across research and engineering to implement safety and performance improvements at scale.
This role involves building and optimizing the machine learning infrastructure that supports Anthropic’s reinforcement learning research. You'll work closely with researchers to improve training efficiency, debug performance bottlenecks, and develop tooling that accelerates model development. The position focuses on high-leverage platform improvements that scale across the entire research team.
This role involves conducting research and engineering to advance large language models, focusing on model architecture, optimization, and scalable training infrastructure. You'll work across the full stack—from low-level system optimizations to high-level model design—while contributing to scientific experiments and tooling. The position emphasizes collaboration, interpretability, and AI safety within a cohesive research team.
This role involves owning critical parts of Anthropic's production pretraining pipeline for large language models, focusing on reliability, performance optimization, and debugging across hardware, networking, and training dynamics. The engineer will run experiments to improve training efficiency, respond to incidents during model launches, and collaborate closely with research and systems teams. It’s a high-impact, operational role blending deep technical engineering with empirical research in a fast-paced, safety-focused AI environment.
This role involves designing and running large-scale reinforcement learning experiments to understand scaling effects across model size, compute, and task horizon. The engineer will build benchmarks for long-horizon RL, debug system-level issues that emerge at scale, and translate validated research findings directly into production training pipelines. The position sits at the intersection of research and systems engineering, requiring deep empirical rigor and close collaboration with both research and infrastructure teams.
This role involves owning and scaling Kubernetes control plane components to support Anthropic's large-scale AI training infrastructure. You'll extend the scheduler for topology-aware and gang scheduling of ML workloads, operate core cluster services like service discovery, and ensure high availability across multi-cloud accelerator fleets. The work is deeply technical, focusing on reliability and performance at extreme scale in collaboration with research and inference teams.
