Technical Program Manager, Research at Anthropic

Hybrid - San Francisco, CA

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Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. As a Technical Program Manager for Research, you will define and build the programs that research teams need most across domains such as compute, evals, RL environments, and emerging initiatives. You will embed deeply within research domains, drive end‑to‑end execution of complex initiatives, establish processes and frameworks, lead large‑scale compute planning, and act as the connective tissue between research, engineering, and product teams to accelerate execution and reduce chaos.

Salary

USD 365,000 - 435,000

Requirements

Skills

  • 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
  • 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
  • Track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization
  • 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
  • Passionate about the potential impact of AI and committed to developing safe and beneficial systems
  • Excited to redefine what technical program management looks like at the frontier of AI research

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

Technologies

ML researchlarge‑scale distributed systemscompute resource planningevaluation (evals)RL environments

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