Lead design and build of core infrastructure for enterprise ML: model evaluation/deployment, inference/serving, data storage/retrieval, and production deployment of large-scale reasoning agents. Work with enterprise customers, integrate on-prem systems, and mentor scientists to adopt strong engineering practices and reliability-focused ownership.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.