Technical Program Manager, Research
Leads technical programs across AI research areas like compute, evals, and RL environments. Drives execution of complex initiatives, establishes processes, and coordinates between research, engineering, and product teams. Requires ML background and experience building programs from scratch.
About the job
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
- Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives
- Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams
- Establish processes and frameworks that bring structure to unstructured research environments
- Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization
- Drive eval readiness for model launches by standardizing results, shaping eval plans, improving tooling, and ensuring honest reporting
- Own execution and operational health of RL environments across major training runs
- Equip research leadership to make decisions quickly by going deep on technical tradeoffs
- Act as the connective tissue between research, engineering, and product teams
You May Be a Good Fit If You
- 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
- Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities
- 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
- Have excellent stakeholder management skill and the ability to influence senior technical staff
- Are comfortable with high-stakes environments
Compensation
Annual Salary: $365,000—$435,000 USD
Skills
Machine Learning, Rl Environments, Compute Infrastructure, Model Evaluation, Distributed Systems, Training Infrastructure, Stakeholder Management, Incident Response, Performance Optimization
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