BioStack Platforms

About BioStack 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. What You’ll Do Own customer success and data delivery end-to-end, from first conversation through scoped dataset delivery Work closely with AI labs, human data companies, and strategic customers to understand exactly what they need and translate that into actionable data plans Engage with hospitals, clinics, imaging groups, and global medical institutions to source and evaluate clinical datasets Develop strong taste for what makes medical data useful for AI training, evaluation, and post-training workflows Help BioStack think through dataset design, quality control, provider selection, and delivery planning Stay close to how frontier research in AI for medicine is evolving, and use that understanding to guide customer conversations and internal decisions Operate across time zones, manage follow-ups tightly, and stay highly responsive in fast-moving customer and partner situations Travel extensively when needed and be comfortable being on call for critical customer or provider conversations Why This Role Is Exciting You’re entering AI for medicine at the perfect moment AI labs are moving quickly into healthcare, but the bottleneck is no longer just models. It is access to the right data, in the right format, with the right taste and judgment behind it. This role puts you at the center of that shift. Work with the world’s best on real problems You will engage directly with frontier AI labs, human data companies, and medical data providers across the world. Your work will influence how leading teams train and evaluate models on real clinical workflows. Own deeply hands-on, high-stakes work This is not a slide-making role. You will be in the weeds with customers and partners, figuring out what data is actually useful, how it should be structured, what quality looks like, and how to get it delivered under real constraints. Shape what we build next Your judgment will directly shape BioStack’s clinical data products, sourcing strategy, and customer deployments. This is a foundational role with room to grow into broader customer ownership, product leadership, or strategic operations. Who You Are You are high agency, highly reliable, and comfortable operating in ambiguity. You can independently push things forward, build trust quickly, and keep many moving pieces aligned without needing heavy structure. You’ve already proven you can operate (2+ years): Customer-facing technical roles High-intensity environments such as OpenAI, Google, Mercor, Turing, top startups, commissioning, or similar Or a clear track record of excellence such as being promoted quickly or recognized as a top performer You likely have: Experience in healthcare, biotech, data, operations, or other demanding customer- or partner-facing roles Strong communication skills and very good follow-through Good judgment around what customers actually want, even when they do not express it clearly Comfort working across cultures, institutions, and time zones Excitement about travel and external-facing work A strong plus if you: Understand how data is generated and used inside medical settings such as ICUs, ERs, radiology departments, or inpatient workflows Have intuition for pain points inside hospitals, clinics, or health systems Have technical depth in healthcare data, medical AI, or data opera

About BioStack 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. About the Role 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. You might thrive in this role if: You are excited by the idea of applying frontier RL methods to healthcare, medicine, and biological data. You have experience with reinforcement learning, language model post-training, agent environments, reward modeling, evaluation, or related ML systems. You have strong taste in data: you can look at a dataset and quickly assess whether it is useful, noisy, biased, underpowered, poorly labeled, or capable of supporting meaningful model improvement. You can evaluate datasets for signal quality, clinical relevance, label fidelity, longitudinal depth, coverage, edge cases, and suitability for RL environments. You can move quickly from research concept to working prototype, then iterate based on empirical results. You are comfortable designing controlled experiments, building baselines, and drawing trustworthy conclusions from noisy real-world data. You like working with complex datasets, including clinical notes, labs, imaging, ECGs, longitudinal patient histories, and expert annotations. You are comfortable working in large ML codebases and can debug training runs, data pipelines, eval harnesses, and model behavior. You care about building systems that are technically rigorous, clinically grounded, and useful beyond demos. You are a self-starter who can own ambiguous problems, define the right technical path, and drive projects to completion. You thrive in a fast-moving startup environment where research, engineering, product, and customer needs all intersect.