BioStack is building the data layer for AI-native healthcare and drug discovery. We work with leading AI labs, human data companies, and frontier biotech teams to source, structure, and deliver high-value clinical and preclinical datasets for model training, evaluation, and deployment.
We sit at the intersection of healthcare, frontier AI, and data infrastructure. Our work spans medical institutions, clinics, imaging centers, and data partners globally, turning messy real-world clinical workflows into AI-ready products that matter.
BioStack is backed by Y Combinator, Afore Capital, Verdict Capital, Heroic VC, and high-profile angels from Meta and Google DeepMind.
As an RL Engineer at BioStack, you will help build the reinforcement learning infrastructure for healthcare AI.
BioStack is building the data engine and RL environment layer for medical AI systems. We source high-value clinical datasets, structure them into model-ready workflows, build benchmarks and reward functions, and create healthcare-specific environments where agents can learn to reason, decide, and improve against verifiable outcomes.
This role sits at the core of that effort. You will work on designing, training, evaluating, and scaling RL systems for real healthcare workflows, including clinical reasoning, chronic disease management, longitudinal patient care, medical data annotation, diagnostic decision-making, and biomedical research tasks.
We’re looking for someone with strong reinforcement learning and ML engineering experience, a bias toward fast iteration, and strong judgment around data. You should have good taste in what makes a dataset valuable: knowing how to evaluate signal quality, coverage, label reliability, clinical relevance, distributional diversity, failure modes, and whether a dataset can support useful RL tasks, benchmarks, and reward functions.
This is a 6-month contract role, based in San Francisco, CA. We expect this to be an in-person/hybrid role, especially for early team members working closely with the founding team.
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.
Salary
$200,000 - $250,000
Equity
0.5% - 1%
Location
San Francisco, CA, US
