Research / Data Engineer – Post-Training & RL

Rnb Consultancy; San Francisco (CA); Presencial; USD 250 000 - 300 000; Tempo integral

Resumo da oferta

Rnb Consultancy in San Francisco seeks a researcher or engineer who spans ML research and systems engineering to join a seed-stage AI company building long-horizon RL environments and post-training data.

You will own the full post-training loop, including infrastructure, training runs, and data; work on challenging tasks across science and ML research; and scale data generation through smarter tooling.

Qualificações

• Depth in ML research (post-training, RL, evals or interpretability) and strong engineering skills.
• Have post-trained models and built the datasets for it.
• Good eye for data, or the research ability to build that eye fast.
• 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.

Responsabilidades

• 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.

Conhecimentos

ML research depth

Post-training models experience

PyTorch experience

ML pipelines

Data quality assessment

Curiosity and side projects

3-7 years experience

Ferramentas

PyTorch

TensorFlow

Descrição da oferta de emprego

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