MACHINE LEARNING ENGINEER (GENERAL)
ABOUT THE COMPANY
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site
ABOUT THE ROLE
You'll build and maintain the ML systems and pipelines that our research runs on top of data pipelines, training infrastructure, evaluation tooling, deployment, observability. The work bridges research and production, and you'll be the person who makes "we ran an experiment" actually mean "we ran it correctly, at scale, with results we trust."
This is a senior ML engineering role. You'll own systems end-to-end. You'll work with researchers daily and translate research code into infrastructure that the team can rely on. You'll move fast and you'll be measured on whether your systems make the team faster.
WHAT YOU'LL DO
- Build and maintain the training, evaluation, and deployment pipelines that our research runs on
- Take research code from prototype to production: refactor, harden, instrument, test
- Design observability into our ML systems (metrics, logs, traces, eval dashboards) so failures surface fast
