Technical Program Manager, Research

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Anthropic's research organization works across the full model development lifecycle, from pre-training and post-training to alignment, interpretability, and safety, each operating at the frontier of AI development. As a Technical Program Manager for Research, you'll define and build the programs that research teams need most. You'll move across research areas like compute, evals, RL environments, and emerging research initiatives, going deep enough in each to understand how researchers work and what they need. You'll identify where the biggest opportunities for impact lie, find the highest-leverage gaps, and build the programs, processes, and tooling that allow researchers to focus on research. This is a 0-to-1 role: you'll explore new domains as priorities shift, determine what each one needs, and create lasting impact where none existed before. Note: This role may require responding to incidents on short-notice, including on weekends.

Responsibilities

• Embed deeply within a research domain to understand the technical landscape, build trust with researchers and technical leaders, and identify the highest-leverage problems to solve, knowing the surface area will shift over time as research priorities evolve
• Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives, picking up new domains quickly and getting to depth fast
• Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams, often without established playbooks or precedent
• Establish processes and frameworks that bring structure to unstructured research environments without slowing researchers down
• Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization across research and production workstreams
• Drive eval readiness for model launches by standardizing results, shaping eval plans early, improving tooling, and ensuring honest, transparent reporting across research, product, and marketing
• Own execution and operational health of RL environments across major training runs, coordinating cross-team trade-offs and feeding insights back into roadmap planning
• Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations
• Act as the connective tissue between research, engineering, and product teams to reduce chaos and accelerate execution

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