Founding Research Engineer, RL/Reasoning
About the role
Role focused on building reinforcement learning environments and post-training systems for healthcare AI at BioStack Platforms.
Responsibilities
- Build healthcare-specific RL environments, including tasks, action spaces/tool interfaces, reward functions, verifiers, and evaluation harnesses.
- Run post-training experiments on language models and agents using techniques such as SFT, RLVR, RLHF/RLAIF, and reward modeling.
- Turn clinical and biomedical datasets into training environments with measurable, verifiable outcomes.
- Design rewards and verifiers that capture correctness across clinical reasoning and longitudinal decision-making tasks.
- Train and evaluate multi-step agents operating across patient histories, clinical tools, and structured/unstructured medical data.
- Build scalable pipelines for rollouts, training, evaluation, experiment tracking, and dataset iteration.
- Analyze model failures and use them to improve environments, rewards, datasets, and subsequent training runs.
Qualifications
- 2+ years of experience working on reinforcement learning, post-training, agents, or related ML systems.
Skills
Benefits
- 401(k): Company match
- Health benefits: Medical, dental, and vision insurance
- Meals: Complimentary lunch provided daily at the office
- AI tools: Unlimited budget for AI tools and software
- Relocation & joining bonus: Available based on role and circumstances
About BioStack Platforms
Real world training envs for healthcare AI models
BioStack is building the data engine for healthcare and drug discovery AI. The bottleneck is not models. It is access to high-quality biological data. Clinical and experimental data is fragmented, unstructured, and locked inside hospitals, labs, and CROs, while generating new data is slow and expensive. BioStack fixes this with proprietary clinical and preclinical data pipelines that turn real biomedical workflows into ML-ready training environments. We structure longitudinal multimodal data across imaging, EHR, and experimental assays, then package it for post-training and reinforcement learning so models can learn how research and care actually happen. Instead of static datasets, BioStack gives AI labs workflow-aligned data and environments that improve reasoning, decision-making, and real-world performance in biology and medicine.
More open roles at BioStack Platforms
- Clinical Data Lead3mo agoSan Francisco · Operations