Technical Program Manager, Research

Anthropic•San Francisco, CA

1d•Hybrid

About The Position

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.

Requirements

Have 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
Are a fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers
Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments
Have a track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization
Can reason about technical tradeoffs at depth across model architecture, training infrastructure, evals, or compute efficiency, and translate them into clear decisions for leadership
Have excellent stakeholder management skill and the ability to influence senior technical staff through competence and consistent delivery
Are comfortable with high-stakes environments where decisions impact compute spend, model training timelines, and launch outcomes
Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Are 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