Flagship Ventures
Machine Learning Scientist - Medicinal Chemistry & Lead Optimization
Flagship Ventures, Cambridge, Massachusetts, us, 02140
Machine Learning Scientist - Medicinal Chemistry & Lead Optimization
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. Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You'll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimizationeven when structures are uncertain or unavailableultimately accelerating DMTA cycles and improving candidate quality. What You'll Be Building Ligand-based QSAR modeling Assay-driven hit triage and prioritization Closed-loop DMTA and MPO Synthesis-aware design and retrosynthesis Generative and enumerative libraries SAR mining and explainability Data foundations Rigorous evaluation and deployment Cross-functional partnership What You'll Need to Succeed Strong proficiency in Python and modern ML Deep experience in ligand-based modeling Solid grasp of medicinal chemistry principles Cheminformatics and data tooling Retrosynthesis and synthesis planning Active learning and design-of-experiments Ability to design rigorous, leakage-controlled benchmarks Strong self-starter with excellent attention to detail Demonstrated industry experience or academic achievement Bonus Points For PhD in Chemoinformatics, Medicinal Chemistry, Computational Chemistry, Computer Science, or related field with a strong publication record in ML/drug discovery venues Experience building synthesis-aware generative models and integrating retrosynthesis into design loops Track record improving DMTA cycle time and MPO outcomes in live programs Expertise with MMPA, activity-cliff handling, conformal prediction, and applicability-domain diagnostics in production Experience triaging HTS/DEL data, PAINS/aggregator/covalent liability filters, and off-target/polypharmacology prediction MLOps for cheminformatics: data versioning, experiment tracking, model serving/monitoring, and cloud/HPC scaling Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
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. Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You'll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimizationeven when structures are uncertain or unavailableultimately accelerating DMTA cycles and improving candidate quality. What You'll Be Building Ligand-based QSAR modeling Assay-driven hit triage and prioritization Closed-loop DMTA and MPO Synthesis-aware design and retrosynthesis Generative and enumerative libraries SAR mining and explainability Data foundations Rigorous evaluation and deployment Cross-functional partnership What You'll Need to Succeed Strong proficiency in Python and modern ML Deep experience in ligand-based modeling Solid grasp of medicinal chemistry principles Cheminformatics and data tooling Retrosynthesis and synthesis planning Active learning and design-of-experiments Ability to design rigorous, leakage-controlled benchmarks Strong self-starter with excellent attention to detail Demonstrated industry experience or academic achievement Bonus Points For PhD in Chemoinformatics, Medicinal Chemistry, Computational Chemistry, Computer Science, or related field with a strong publication record in ML/drug discovery venues Experience building synthesis-aware generative models and integrating retrosynthesis into design loops Track record improving DMTA cycle time and MPO outcomes in live programs Expertise with MMPA, activity-cliff handling, conformal prediction, and applicability-domain diagnostics in production Experience triaging HTS/DEL data, PAINS/aggregator/covalent liability filters, and off-target/polypharmacology prediction MLOps for cheminformatics: data versioning, experiment tracking, model serving/monitoring, and cloud/HPC scaling Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.