Lila Sciences is the worldâs first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.
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)
- PyTorch/JAX, geometric learning, generative modeling, experiment tracking, model/data versioning, serving; comfort with hybrid cloud + HPC.
- Statistical mechanics and thermodynamics basics, medicinal chemistry and DMPK fundamentals, assay QC and leakage control; designs prospective, decision-grade evaluations.
- 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).
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; prioritize along live program needs and compute budget.
- 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; publish model cards and decision logs for auditability.
- Co-design schemas, ontologies, and provenance with Assay Informatics, Structural Biology, and Data Platform; ensure reliable ETL from ELN/LIMS, structure, and simulation.
- Partner with ML Engineering to deliver reproducible training, scalable serving (APIs/batch), monitoring, and incident response for scientific services on cloud + HPC.
- Coordinate with partner teams internal and exteral to Lila for assay QC, structural prep, and data platform SLAs; evaluate vendors and open-source where it accelerates impact.
Other
- Lead and scale a cross-functional Scientific ML team
- Hire, mentor, and develop a 6+ person team spanning AI scientists and an ML platform engineer; establish high standards for scientific rigor, code quality, and collaboration.
- Set a high bar for clarity, integrity, and humility; communicate uncertainty and trade-offs to technical and executive stakeholders.
- Hires and grows high-performing teams; sets crisp priorities; aligns diverse stakeholders; communicates clearly at both the whiteboard and the exec table.
- PhD in CS, Computational Chemistry, Chemoinformatics, Biophysics, or related field with publications in top ML/drug discovery venues.
