Post-Training Applied Researcher
Baseten · San Francisco, CA · EPD
About this role
Baseten is hiring a mid-level Applied Scientist in the machine learning function based in San Francisco, CA. The posting calls out experience with LLMs, Reinforcement Learning, Machine Learning, AI Agents. Compensation is listed at $200,000–$275,000 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA
- Department
- EPD
- Posted
- Mar 17, 2026
More roles at Baseten
Job description
from Baseten careersABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.
THE ROLE
This role sits at the applied end of our post-training research efforts. You will work directly with stakeholders from the world’s fastest-growing AI companies to post-train open-source models that outperform frontier closed models on their specialised tasks. Your day-to-day is finding creative ways to extract signal from complex, domain-specific datasets and building the reward functions, environments, eval harnesses, and training pipelines that turn that signal into better models. The models you train ship to production and reach millions of users.
We are looking for people with hands-on LLM fine-tuning and RL experience. Researchers who are excited by the prospect of shipping models into production, who can translate a customer's domain-specific requirements into an effective training curriculum, and who know when to be rigorous and when to iterate fast.