Post-Training Applied Researcher

Job summary

Responsibilities

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.

• 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…
• 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.

What they're looking for

• Hands-on experience training LLMs with reinforcement learning — demonstrated understanding of GRPO or PPO beyond recipe-level reproduction, including group advantage computation…
• Strong intuition for reward engineering: the ability to distinguish between a reward that trains effectively and one that will exploit at scale
• Experience building multi-turn agent environments with tool use, not limited to single-turn question-answering setups
• Comfort working across the full pipeline from dataset construction through training, evaluation, and deployment
• Experience with production ML systems. Preference for candidates who have closed a training–inference loop where production data feeds back into model improvement

Tech stack

• Spark