Senior Scientist: Cheminformatics & Computational Chemistry

Key Responsibilities
End-to-End Workflow Development
Design and implement workflows spanning:
o Virtual screening (ligand-based and structure-based)
o Hit identification and hit expansion
o Hit-to-lead selection
o Lead optimization
Method 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 screening
o Molecular enumeration:
Reaction-based enumeration
Fragment-based design and expansion
o Ligand-based modeling:
QSAR, similarity, clustering, active learning loops
o Structure-based modeling:
Docking, rescoring, pose prediction, structure-aware search
o Physics-based methods:
Molecular dynamics (MD)
Free energy perturbation (FEP) and related approaches
Cross-functional Collaboration
Partner with:
o Machine Learning teams to integrate predictive and generative models
o Software Engineering teams to productionize workflows and ensure scalability
o Scientific stakeholders to align workflows with drug discovery needs

Quals--
Hybrid work schedule. Remote candidates may be considered on a case-by-case basis.
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 searching
o Reaction-based and fragment-based enumeration
o Docking and structure-based design
o QSAR and ligand-based modeling
o Active learning and iterative design strategies
o Physics-based simulations (e.g., MD, FEP)
Hands-on experience with tools such as:
o RDKit, OpenEye, or equivalent
o Docking platforms (e.g., Glide, AutoDock, GOLD)
Strong programming skills in Python
Preferred 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)