Engineering Manager, ML
San Francisco • FullTime
Posted 3mo ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
SpaceXSI is building Super Intelligence to help humanity understand the universe, developing the full Super Intelligence stack from supercomputers to Grok models and a suite of applications. This role will lead a team of engineers building the infrastructure used to train, test, and evaluate our models, where infrastructure and model behavior meet directly. You will set technical direction for training and evaluating models at scale, debug alongside your team, and work with researchers to translate tradeoffs in latency, quality, and cost into built infrastructure.
Responsibilities
- Lead a team of engineers building infrastructure for model training, testing, and evaluation.
- Set technical direction for large-scale model training and evaluation.
- Debug infrastructure and model behavior issues.
- Collaborate with researchers to translate model tradeoffs into infrastructure decisions.
- Build rollout infrastructure for large-scale RL experiments.
- Design evaluation pipelines to catch regressions and provide fast, trustworthy signals.
- Own and maintain model training and testing environments for speed and reproducibility.
- Establish rigorous quality and progress measurement for models.
- Hire, mentor, and develop infrastructure engineering talent.
Requirements
- Experience leading engineering teams that build production ML model training, evaluation, or serving infrastructure.
- Strong infrastructure and distributed systems fundamentals, understanding reliability and performance under load.
- Comfortable writing code, reviewing PRs in depth, and using development tools.
- Ability to operate in ambiguity, ask clarifying questions, and make decisions with incomplete information.
- Proven track record of hiring and developing engineers.
- Ability to communicate effectively with researchers about model behavior and engineers about systems design.
- Hands-on experience with RL training infrastructure, eval frameworks, or building/maintaining simulated environments (bonus).