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
Own data pipelines for training and evaluation: ingest, dedup, version, validate
Work closely with researchers to understand what they need, what's slow, and what's brittle
Set engineering standards across our ML stack (testing, reviews, runbooks) so the team scales
Contribute to architectural decisions that shape how research and production interact
WHAT WE'RE LOOKING FOR
Senior ML engineer with 6+ years building production-grade ML systems
Track record across the full lifecycle: data, training, evaluation, deployment, monitoring
Strong distributed systems experience; you've shipped systems that have to be on
Fluent Python, fluent with at least one of (PyTorch, JAX); comfortable at the systems-level when needed
Comfortable with experimentation infrastructure (Ray, Slurm, Kubernetes, or similar)
Bias toward shipping; you prefer working code over working diagrams
Strong written communication
NICE TO HAVE
Experience building experimentation platforms or research infrastructure at a frontier ML lab
Background in distributed training systems
Open-source contributions to ML infrastructure
History of working effectively with small senior teams
THIS ROLE IS PROBABLY NOT FOR YOU IF
You want to do research with engineering as a side activity: this is engineering as the main thing
Cross-functional work with researchers (translation, scoping, education) doesn't appeal
Long-running ownership of running systems isn't appealing: this role has it
Key team members
John Hess
Zhitao Gong
Daniil Polykovskiy
Steven Abreu
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