AI Research Scientist — Production RL & Data Pipelines

Velocity Tech; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Velocity Tech in San Francisco develops evaluations and training environments for AI labs to test and improve models. The Research Scientist will optimize how much models learn from tasks, maximizing training signal per hour of data produced.

You will combine hands-on RL production work with input from frontier researchers to assess data quality, build new datasets, and shape task distributions. This role directly impacts product impact and revenue growth.

Qualifications

• Hands-on experience running RL in production at a frontier lab.
• Experience taking an LLM through post-training from start to finish.
• Ability to set research direction and build it themselves in a fast-paced environment.
• Genuine fascination with how models learn from data, and how to make every task teach them as much as possible.

Responsibilities

• Partner with researchers at AI labs to develop post-training approaches and better ways of measuring data quality.
• Build and run the internal post-training pipeline to quantify how much the company’s tasks improve models.
• Turn available datasets and domain experts into new data products.
• Finding ways to build compounding feedback loops into how the company works.
• Building agents that can analyse thousands of environments and millions of trajectories.
• Shaping and defining the task distributions for new datasets.
• Working with the procurement team to check that external data is worth refining.
• Designing scalable systems to ingest and assess datasets before purchase.
• Developing new methods for extracting signal from raw data.

Skills

RL in production

LLM post-training

data quality assessment

research direction

Job description

Velocity Tech in San Francisco develops evaluations and training environments for AI labs to test and improve models. The Research Scientist will optimize how much models learn from tasks, maximizing training signal per hour of data produced.

You will combine hands-on RL production work with input from frontier researchers to assess data quality, build new datasets, and shape task distributions. This role directly impacts product impact and revenue growth.