Technical Program Manager, RL Research

The shape of it

In the posting’s own words

As a Technical Program Manager on the reinforcement learning team, you will own the systems and programs that determine how fast our research moves: a trustworthy read on the state of RL research, the review and prioritization processes that turn that read into critical decision for production RL runs. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when research or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity.

What it asks for · 7

• Have a background in ML engineering or ML research before transitioning to technical program management
• Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production
• Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research
• Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers
• Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting
• Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery
• Are excited about pushing the frontier of what RL can do at scale

What the job covers

• 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 set the agenda, make sure the right context is in the room ahead of time, and close 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

Tools and skills named

Words the posting leans on

• research14×
• technical6×
• data4×
• infrastructure4×
• model4×
• program4×
• read4×
• systems4×
• decisions3×
• processes3×
• researchers3×
• background2×
• build2×
• critical2×
• data operations2×
• deep2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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