Research Scientist

Our client is a heavily backed AI Startup that is revolutionizing healthcare AI to radically improve drug discovery and with the goal to cure cancer. Having recently secured a $100M valuation, they are building the foundational data layer and pre & post-training simulation environments where the next generation of medical AI models learn. They are seeking someone to own the full reinforcement learning loop and build the crucial evaluation and reward layer for frontier AI models using real-world clinical and genomic context. Role & Impact Build comprehensive RL environments, encompassing task design, action spaces, tool interfaces, verifiers, and evaluation harnesses. Run sophisticated post-training experiments on language models and agents utilizing techniques like SFT, RLVR, RLHF/RLAIF, and reward modeling. Train and evaluate multi-step agents to navigate complex patient histories and unstructured medical data to capture correctness in clinical reasoning. Analyze model failures to continuously improve the data, rewards, and feedback signals that dictate what these systems learn. Essential Skills 1+ years of highly technical experience focusing on reinforcement learning, agent environments, LM post-training, or related ML systems. Exceptional judgment around data quality, capable of assessing signal fidelity, coverage, and clinical relevance to support meaningful training tasks. Strong ability to build scalable pipelines, debug complex training runs within large ML codebases, and move rapidly from research concepts to working prototypes.