Computational Chemist

We are seeking a highly motivated Cheminformatics Scientist to design, evaluate, and build end-to-end small molecule computational workflows within the TuneLab platform. This role requires strong hands-on expertise in state-of-the-art computational methods across the drug discovery pipeline, from virtual screening to lead optimization.You will work at the intersection of cheminformatics, physics-based modeling, and machine learning, partnering closely with TuneLab software engineering and ML teams to deliver scalable, production-grade workflows.This is a high-impact contract role, where you will be expected to independently evaluate tools, make build-vs-buy decisions, and deliver robust workflow solutions.Required Qualifications:• PhD or MS in Cheminformatics, Computational Chemistry, Medicinal Chemistry, or related field• Strong understanding of small molecule drug discovery workflows• Demonstrated expertise in:o Substructure and similarity search (fingerprints, graph-based, embedding-based)o Shape and pharmacophore searchingo Reaction-based and fragment-based enumerationo Docking and structure-based designo QSAR and ligand-based modelingo Active learning and iterative design strategieso Physics-based simulations (e.g., MD, FEP)• Hands-on experience with tools such as:o RDKit, OpenEye, or equivalento Docking platforms (e.g., Glide, AutoDock, GOLD)• Strong programming skills in PythonPreferred Qualifications• Experience working with ultra-large chemical libraries (e.g., Enamine REAL, WuXi Galaxy)• Familiarity with generative chemistry approaches (SMILES-, graph-, or diffusion-based models)• Experience integrating ML models into production workflows• Experience with workflow orchestration tools (e.g., Airflow, Nextflow)Key ResponsibilitiesEnd-to-End Workflow Development• Design and implement workflows spanning:o Virtual screening (ligand-based and structure-based)o Hit identification and hit expansiono Hit-to-lead selectiono Lead optimizationMethod Development & Application• Apply and integrate core computational chemistry and cheminformatics methods, including:o Ultra-large library search: Substructure search Fingerprint and embedding-based similarity search Shape and pharmacophore-based screeningo Molecular enumeration: Reaction-based enumeration Fragment-based design and expansiono Ligand-based modeling: QSAR, similarity, clustering, active learning loopso Structure-based modeling: Docking, rescoring, pose prediction, structure-aware searcho Physics-based methods: Molecular dynamics (MD) Free energy perturbation (FEP) and related approachesCross-functional Collaboration• Partner with:o Machine Learning teams to integrate predictive and generative modelso Software Engineering teams to productionize workflows and ensure scalabilityo Scientific stakeholders to align workflows with drug discovery needs