The Technical Program Manager, RL Research role at Anthropic focuses on driving the reinforcement learning research pipeline from pre‑training through post‑training. The position requires a strong background in ML engineering or research, deep technical knowledge of data pipelines and RL systems, and the ability to lead cross‑functional teams across research, infrastructure, product, and data operations. The TPM will own program execution, prioritize experiments, establish processes, and collaborate with stakeholders to accelerate research and production runs in a hybrid, on‑site environment.
Technical Program Manager, RL Research at Anthropic
Hybrid - San Francisco, CA, New York City, NY
More jobs at AnthropicSalary
USD 365,000 - 435,000
Requirements
Skills
- Background in ML engineering or ML research before transitioning to technical program management
- Deep, hands‑on experience with ML training pipelines, RLHF systems, and large‑scale data infrastructure in production
- Track record of building execution plans and inventing high‑leverage processes that reduce operational overhead and let researchers focus on research
- Fast learner who builds deep contextual understanding in unfamiliar technical domains
- Resourceful, high‑agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast‑moving research setting
- Excellent stakeholder management and communication skills, with the ability to influence senior technical staff
- Excited about pushing the frontier of what RL can do at scale
- Strong technical depth: ability to debug data pipelines, read RL transcripts, and make allocation and quality decisions in real time
Responsibilities
- Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day‑to‑day health, and incidents
- Work with RL org leads on prioritizing, ranking, and tracking the state of experiments
- Drive research reviews end to end in partnership with setting the agenda, ensuring the right context in the room, and closing the loop on what gets decided
- Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down
- Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade‑off decisions
Technologies
ML training pipelinesRLHF systemsLarge‑scale data infrastructure
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