Technical Program Manager, Research

About the role

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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.
• 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.

Qualifications

• A background in ML research or engineering with several years of experience building technical programs from scratch, ideally with hands-on exposure to training, evaluation, or large-scale distributed systems.
• A fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers.
• Resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments.
• A track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization.
• The ability to reason about technical tradeoffs at depth across model architecture, training infrastructure, evals, or compute efficiency, and translate them into clear decisions for leadership.
• Excellent stakeholder management skill and the ability to influence senior technical staff through competence and consistent delivery.
• Comfortable with high-stakes environments where decisions impact compute spend, model training timelines, and launch outcomes.
• A passion for the potential impact of AI and a commitment to developing safe and beneficial systems.
• An excitement to redefine what technical program management looks like at the frontier of AI research.