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.

Dense, on-policy or both?

Repeated kv cache for long-running agents

Distillation without the dark – replicating black-box on-policy distillation on Baseten

Design and run post-training pipelines: SFT, GRPO, DPO, RLVR, reward function engineering, and synthetic data generation.

Build task-specific training environments and evals tailored to customer domains like healthcare, code generation, and legal, spanning multi-turn tool use, sandboxed execution, and agentic workflows.

Work directly with customers to translate production data into training signal, designing reward loops from real usage patterns and handling distribution shift.

Run and analyze training experiments end-to-end: diagnose reward hacking, importance sampling drift, and advantage estimation instabilities.

Publish findings at top venues and contribute to Baseten's open-source training libraries.

Post-Training Applied Researcher

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Baseten

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