Applied ML Scientist (Staff / Principal)

About the Team Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases. We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. You will work side-by-side with top multidisciplinary researchers to design and build generative foundation models at scale, having access to ample compute and large-scale simulations. About the Role This unique role is for a scientist who is passionate about being a catalyst for applying cutting-edge AI to solve real-world drug discovery challenges. You will be the critical bridge between our long-term research and our experimental drug discovery programs. Your mission is to build, evaluate, monitor, and improve our state-of-the-art models directly into active drug programs, leading the charge on model validation, deployment, and analysis to guide the discovery of new medicines. You will act as both a translator and a strategist, ensuring our research is aimed at the most critical challenges and that our drug hunters can leverage the full power of our industry-leading AI platform. This role requires a deep understanding of cheminformatics, computational chemistry, and experimental techniques, strong data science skills, and a talent for communicating complex ideas to a diverse, multidisciplinary team. Positions are available at various levels of seniority: Senior, Staff, and Principal. What You’ll Do - Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality. - Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty. - Evaluate model quality by validating predictions against project data and internal or external benchmarks. - Curate internal and external datasets for model training and validation (in collaboration with experimental teams). - Contribute to design and analysis of experiments on model changes and alternative architectures. You are - A seasoned computational scientist with a proven track record of machine learning based methods to impact small molecule drug discovery projects. - A cheminformatics expert, fluent in the language of molecular data with hands-on mastery of tools like RDKit or OpenEye. - A scientist who speaks the language of experimental drug discovery, with a strong familiarity with common assay types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.). - A rigorous data scientist, with experience inmodeling and analysis of small molecule datasets and passion for statistical validation, uncertainty quantification, and deriving clear insights from complex, noisy data. - A hands-on applied scientist and software engineer with strong coding skills in Python and a deep practical knowledge of the applied ML toolkit (e.g., scikit-learn, PyTorch). - An exceptional communicator and collaborator, able to act as the bridge between machine learning researchers and experimental scientists. - A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. - A true team player who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined. - Inspired by our culture of intellectual curiosity and the shared belief that breakt