Research Engineer
Goliath Partners · San Francisco Bay Area
Apply & track with Apply EdgeOur client is a hyper-growth, heavily backed startup that is revolutionizing the frontier of healthcare AI to radically improve drug discovery and solve cancer. Having recently secured a $100M raise, they are building the foundational data layer and post-training simulation environments where the next generation of medical AI models learn. They are seeking a powerhouse Research Engineer 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 & ImpactBuild 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 Skills1+ 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.