RL Research TPM - Lead End-to-End AI Programs

Anthropic Limited; New York (NY); Hybride; USD 365 000 - 435 000; Plein temps

Avantages offerts par ce poste

Competitive compensation and benefits

Equity donation matching (optional)

Generous vacation and parental leave

Flexible working hours

Office space

Résumé du poste

Anthropic is seeking a Technical Program Manager for the reinforcement learning team. You will own systems and programs that accelerate research, providing a trustworthy read on RL progress and coordinating prioritization across research, infrastructure, product, and data operations.

You should bring a strong ML engineering or research background, deep experience with ML training pipelines and RLHF systems, and a proven ability to lead cross-functional initiatives while maintaining velocity in a

Qualifications

• Background in ML engineering or ML research.
• Hands-on experience with ML training pipelines and RLHF systems.
• Proven ability to build execution plans and reduce operational overhead.

Responsabilités

• Deliver a regular read on 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
• Lead end-to-end research reviews, set the agenda, ensure context is in the room, and close the loop on decisions
• 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 and make technical trade-offs

Connaissances

ML engineering

ML research

Stakeholder management

Program leadership

Data infrastructure

RLHF systems

Communication skills

Ambiguity navigation

Formation

Bachelor’s degree

Description du poste

Anthropic is seeking a Technical Program Manager for the reinforcement learning team. You will own systems and programs that accelerate research, providing a trustworthy read on RL progress and coordinating prioritization across research, infrastructure, product, and data operations.

You should bring a strong ML engineering or research background, deep experience with ML training pipelines and RLHF systems, and a proven ability to lead cross-functional initiatives while maintaining velocity in a