Job description

Researcher or engineer? This role wants both, in one person. A profitable, seed-stage AI company in San Francisco creates long-horizon reinforcement learning environments and post-training data for leading AI labs in the US and abroad. Most data providers solve demand by adding people. This team treats post-training data as a research problem and scales it with processes that improve themselves. Demand for their data is bigger than what they can deliver today, so they're growing a small, very strong core team of researchers and engineers. What you'll own • The full post-training loop: infrastructure, training runs and the data that goes into them • Long-horizon RL environments and tasks in many fields, from science to ML research itself • Scaling data generation through smarter, self-improving tooling, not more headcount • Judging which real-world tasks are worth building, because they matter to the labs and the economy What you bring • Real depth in ML research (think post-training, RL, evals or interpretability) and serious engineering skills • You've post-trained models yourself and built the datasets for it • A good eye for data, or the research ability to build that eye fast • You read code fast and solve problems quickly in PyTorch and ML pipelines • Curiosity that shows: papers, side projects, open source contributions or writing • Roughly 3–7 years of experience Bonus points • Both published research and things you actually shipped • Domain depth next to ML, such as neuroscience, physics, chemistry or systems • Artefacts people use: fast libraries, benchmarks or well-cited papers What's in it for you • $250k–$300k base • Work that goes straight into how frontier models are trained • A small, senior team where research and engineering are one job • Open to visa holders as well as US citizens and green card holders Good to know • Full-time, on-site in San Francisco, 9-9-6 • Process: first call with a founder → technical interview (a standalone problem, with an ML version in PyTorch) → work trial → offer Does this sound like you? Apply now. If you're a match, we'll be in touch within 3 working days. RnB Consultancy | Netherlands | 3604 AB Maarssen | KvK 71489703

Requirements

Must-haves

Researcher or engineer? This role wants both, in one person.
What you'll own
The full post-training loop: infrastructure, training runs and the data that goes into them
Long-horizon RL environments and tasks in many fields, from science to ML research itself
Scaling data generation through smarter, self-improving tooling, not more headcount
Judging which real-world tasks are worth building, because they matter to the labs and the economy
Real depth in ML research (think post-training, RL, evals or interpretability) and serious engineering skills
You've post-trained models yourself and built the datasets for it
A good eye for data, or the research ability to build that eye fast
You read code fast and solve problems quickly in PyTorch and ML pipelines
Curiosity that shows: papers, side projects, open source contributions or writing
Roughly 3–7 years of experience

Nice-to-haves

Both published research and things you actually shipped
Domain depth next to ML, such as neuroscience, physics, chemistry or systems
Artefacts people use: fast libraries, benchmarks or well-cited papers

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: manatal
Effort: medium