Senior Principal / Associate Director, Scientific ML for Drug Discovery

🚀 About Lila

Lila Sciences is the world’s 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. Learn more about this mission at  www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

🌟 Your Impact at Lila

Lead and scale a cross-functional Scientific ML team that delivers end-to-end impact on real programs. You will be the player–coach setting technical direction across AI structure-based design, ligand-based optimization, synthesis planning, ADMET/PK modeling, and AI-accelerated physics, while partnering with ML platform engineering to ship reliable, production-grade services. Your leadership will turn diverse data and models into a cohesive, closed-loop design engine that shortens DMTA cycles, improves hit rate and MPO, and de-risks program decisions.

🛠️ What You'll Be Building

• Strategy and roadmap: 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.
• Team building: 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.
• Unified design loop: Orchestrate a synthesis-aware, MPO-constrained, uncertainty-calibrated design workflow that fuses assay-driven ligand models with structure/physics signals and ADMET/PK constraints.
• Evaluation governance: Institute leakage-safe datasets and splits (scaffold/time/series), prospective validations, OOD tests, and model gating; publish model cards and decision logs for auditability.
• Data contracts and foundations: Co-design schemas, ontologies, and provenance with Assay Informatics, Structural Biology, and Data Platform; ensure reliable ETL from ELN/LIMS, structure, and simulation.
• Productionization: Partner with ML Engineering to deliver reproducible training, scalable serving (APIs/batch), monitoring, and incident response for scientific services on cloud + HPC.
• External collaboration: 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.
• Culture and communication: Set a high bar for clarity, integrity, and humility; communicate uncertainty and trade-offs to technical and executive stakeholders.

🧰 What You’ll Need to Succeed

• 8+ years (post-PhD or equivalent) building and shipping ML for drug discovery or closely related domains; demonstrated impact on live programs
• Technical depth and breadth: 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 (Δ-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.

✨ Bonus Points For

• 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).

🌈 We’re All In