Lila Sciences is solving humankind's greatest challenges by applying AI to life, chemistry, and materials science, aiming to enable scientists to bring solutions in human health, climate, and sustainability at an unprecedented pace and scale.
Requirements
- 8+ years (post-PhD or equivalent) building and shipping ML for drug discovery or closely related domains; demonstrated impact on live programs.
- Expertise in at least two of the following and fluency across the rest: AI SBDD (equivariant/3D graph models for pose/affinity, pocket embeddings), Ligand-based QSAR/ADMET and active learning for hit-to-lead/lead opt, Synthesis planning and reaction/condition/yield modeling, ADMET/PK/PD (IVIVE, PBPK/QSP) and uncertainty/calibration, ML-for-simulation/free energy (Î-learning surrogates, learned force fields).
- ML engineering excellence: PyTorch/JAX, geometric learning, generative modeling, experiment tracking, model/data versioning, serving; comfort with hybrid cloud + HPC.
- Scientific rigor: Statistical mechanics and thermodynamics basics, medicinal chemistry and DMPK fundamentals, assay QC and leakage control; designs prospective, decision-grade evaluations.
- Leadership: Hires and grows high-performing teams; sets crisp priorities; aligns diverse stakeholders; communicates clearly at both the whiteboard and the exec table.
Responsibilities
- Lead and scale a cross-functional Scientific ML team that delivers end-to-end impact on real programs.
- Define the technical vision and quarterly milestones for SBDD, ligand-based QSAR/ADMET, synthesis planning, and physics-ML.
- Hire, mentor, and develop a 6+ person team spanning AI scientists and an ML platform engineer.
- Orchestrate a synthesis-aware, MPO-constrained, uncertainty-calibrated design workflow that fuses assay-driven ligand models with structure/physics signals and ADMET/PK constraints.
- Institute leakage-safe datasets and splits (scaffold/time/series), prospective validations, OOD tests, and model gating.
- Co-design schemas, ontologies, and provenance with Assay Informatics, Structural Biology, and Data Platform.
- Partner with ML Engineering to deliver reproducible training, scalable serving (APIs/batch), monitoring, and incident response for scientific services on cloud + HPC.
Other
- PhD in CS, Computational Chemistry, Chemoinformatics, Biophysics, or related field with publications in top ML/drug discovery venues.
- Delivered unified design loops that improved hit rate/MPO and reduced cycle time; experience integrating retrosynthesis and PBPK into optimization.
- Open-source leadership (e.g., RDKit/Chemprop/DeepChem, PyTorch Geometric/e3nn, OpenMM) or vendor evaluation/deployment experience.
- Experience with HTS/DEL analytics, structural bioinformatics (AlphaFold/ensembles), or regulated documentation (model qualification).
