Our client is a YC-backed AI startup working to transform drug discovery, with a mission as ambitious as it gets: helping cure cancer. They’re building the foundational data layer and the pre- and post-training simulation environments where the next generation of medical AI models will learn.
They’ve raised a $10M pre-seed at a $100M valuation, backed by angel investors including executives from OpenAI and Anthropic. Now they’re assembling the founding team, and this is one of the first seats at the table.
You’ll own the full reinforcement learning loop and build the evaluation and reward layer that teaches frontier models to reason correctly about real-world clinical and genomic data. In healthcare, the difference between a model that sounds right and one that is right matters enormously, and you’ll be the one defining that difference.
What you’ll do
Build RL environments end to end, including task design, action spaces, tool interfaces, verifiers, and evaluation harnesses
Run post-training experiments on language models and agents using SFT, RLVR, RLHF/RLAIF, and reward modeling
Train and evaluate multi-step agents that navigate complex patient histories and unstructured medical data, capturing what correct clinical reasoning actually looks like
Dig into model failures and use what you find to improve the data, rewards, and feedback signals that shape what these systems learn
Help set the technical direction, culture, and hiring bar as a founding team member
What you bring
1+ years of deeply technical work in reinforcement learning, agent environments, LM post-training, or related ML systems, whether in industry, research, or serious independent projects
Strong judgment about data quality: you can assess signal fidelity, coverage, and relevance, and you know what makes a training task meaningful
The ability to build scalable pipelines, debug complex training runs in large ML codebases, and move fast from research idea to working prototype
Nice to have
Experience with healthcare, clinical, biological, or genomic data (not required; you’ll pick up the domain quickly alongside the team)
Publications, open-source work, or projects in RL, evals, or post-training
Prior early-stage startup experience
Why this role
A mission that matters: your work goes directly toward faster drug discovery and better cancer treatment
Founding-team ownership: shape the core technology and the company itself from day one
Serious backing: YC, a $10M pre-seed at a $100M valuation, and angels from OpenAI and Anthropic leadership
Frontier problems: RL environments and reward design for medical AI is one of the least solved and most important areas in the field
Top-tier compensation: up to $400k base, up to 1.0% equity, comprehensive benefits, and an unlimited AI tools budget
Don’t tick every box? Please apply anyway. Some of the best people in RL and post-training took unconventional paths, and we’d rather hear from you than have you rule yourself out.
Interested? Reply and Goliath Partners will be in touch.
