Founding Engineer - Foundational Models

CT19 are partnering with an early stage start up operating at the intersection of BioTech and AI, who are building a core intelligence layer for biology.Their technology has been developed through leading academic and industry collaborations and published in top machine learning venues and within a few months of spinning out, their models are already being adopted by top 10 pharma companies, on challenges such as response prediction, biomarker discovery, and scientific reasoning across complex biological datasets. About the RoleWe are supporting the search for a Founding Research Engineer to help build and scale the systems that power the company’s models.This position sits at the intersection of research and engineering, focusing on training, post-training, evaluation, performance optimisation, and the infrastructure required to support these processes. ResponsibilitiesBuild and improve training and post-training systems for biological foundation models and agent-based workflowsDesign and run experiments across supervised fine-tuning, reinforcement learning, tool use, evaluation, and model behaviour optimisationDevelop and maintain distributed RL and post-training infrastructureImprove reliability of rollout, evaluation, and reward pipelinesIdentify and resolve performance bottlenecks across GPU, networking, and storage layersCollaborate with founders and domain experts to translate biological problems into model tasks and evaluation frameworksContribute to translating research advancements into tangible product and customer impactCandidate ProfileProven experience training or significantly improving advanced LLMs or generative ML systemsStrong software engineering and distributed systems expertiseDeep proficiency in Python and modern ML frameworks such as PyTorch, JAX, or similarExperience with reinforcement learning or post-training methodologiesBackground in building evaluation systems for tool-using or open-ended modelsStrong understanding of GPU performance constraints and memory trade-offsExperience diagnosing and resolving performance issues in production ML environmentsComfortable balancing research exploration with engineering executionComfortable operating in a fast-moving, early-stage environment