Post-Training Applied Researcher

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.

Responsibilities

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

Requirements

Hands-on experience training LLMs with reinforcement learning, with demonstrated understanding of GRPO or PPO beyond recipe-level reproduction, including group advantage computation, clipped objectives, and KL penalty design; Strong intuition for reward engineering, including distinguishing a reward that trains effectively from 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, with preference for candidates who have closed a training–inference loop where production data feeds back into model improvement

Nice to have

Experience with RL training frameworks; Publications at NeurIPS, ICML, ICLR, focused on RL for LLMs, reward modeling, or alignment

Benefits

Competitive compensation, including meaningful equity; (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents; Flexible PTO policy including company wide Winter Break; Paid parental leave; Fertility and family-building stipend through Carrot; (U.S. only) Company-facilitated 401(k); Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities