Lila Sciences · 2 months ago
Machine Learning Scientist I/II, Medicinal Chemistry & Lead Optimization
Cambridge, MA USA
Full-time
Onsite
Entry, Mid Level
$176K/yr - $304K/yrLila Sciences is pioneering a new age of boundless discovery by building capabilities to apply AI to every aspect of the scientific method. The Machine Learning Scientist I/II will join the Drug Discovery group to develop AI tools that optimize medicinal chemistry processes and improve candidate quality through ligand-based modeling and data-driven design strategies.
Artificial Intelligence (AI)Life ScienceSoftware
Responsibilities
Develop multi-task and transfer-learned models for potency, selectivity, and developability using graph/message-passing, and conformer-aware features
Build models that learn from HTS, DEL, and follow-up assays; robust curve-fitting, plate/batch effect correction, dose–response QC, and time-split/scaffold-split evaluations to ensure prospective reliability
Create active learning and Bayesian optimization strategies to propose the next best analogs under multi-parameter objectives
Integrate template-based and template-free retrosynthesis with reaction prediction, condition and yield modeling, building-block availability, and cost/time/risk scoring
Build BRICS/RECAP/fragment-linking enumerations and property-conditioned generative models that respect synthetic constraints and matched molecular pair rules for local SAR exploration and scaffold hopping
Automate MMP analysis, local SAR maps, and substructure attributions to surface chemist-actionable insights; link assay deltas to specific modifications and highlight potential bioisosteres and de-risking moves
Establish cheminformatics pipelines for standardization, deduplication, structure normalization, and assay/ELN/LIMS ingestion; define ontologies and metadata for traceability and reproducibility
Design leakage-safe splits, conformal prediction for calibrated decisions, and prospective tests. Ship APIs and tools that integrate with medchem workflows, procurement, and automated synthesis
Work closely with medicinal chemists, DMPK, biology, and automation to translate TPPs into modeling objectives and to operationalize model recommendations in real make–test cycles
Qualification
Required
Strong proficiency in Python and modern ML (PyTorch/JAX/TF, scikit-learn, XGBoost/CatBoost), with experience training at scale and deploying end-to-end pipelines
Deep experience in ligand-based modeling (QSAR/QSPR, multi-task learning, uncertainty and applicability domain, calibration) and ADMET prediction for medicinal chemistry
Solid grasp of medicinal chemistry principles: SAR development, bioisosteres, property tuning (pKa/logD/PSA), selectivity design, and liability mitigation (CYP, hERG, reactivity, permeability, solubility)
Cheminformatics and data tooling: RDKit, Chemprop/DeepChem, conformer generation, fingerprints/descriptors, ELN/LIMS integration, and assay data processing/curve-fitting
Retrosynthesis and synthesis planning: Familiarity with template-based/template-free methods, route scoring, reaction/yield/condition prediction, building block catalogs, and makeability constraints
Active learning and design-of-experiments: Bayesian optimization, diversity sampling, and portfolio-aware selection under experimental and synthesis budgets
Ability to design rigorous, leakage-controlled benchmarks and prospective validations; experience with scaffold/time splits and activity-cliff-aware evaluation
Strong self-starter with excellent attention to detail and clear communication; able to collaborate tightly with chemists and biologists
Demonstrated industry experience or academic achievement
Preferred
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; familiarity with tools like ASKCOS/AiZynth-style planners or equivalent
Track record improving DMTA cycle time and MPO outcomes in live programs; integration with procurement and automated synthesis platforms
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
Benefits
Bonus potential
Generous early equity
Company
Lila Sciences
Lila Sciences creates a scientific superintelligence platform and autonomous labs for life sciences, chemistry, and materials science. It is a sub-organization of Flagship Pioneering.
Founded in 2023
201-500 employees
H1B Sponsorship
Lila Sciences has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (8)
Funding
Current Stage
Growth StageTotal Funding
$550MKey Investors
NVenturesFlagship Pioneering
2025-10-14Series A· $115M
2025-09-14Series A· $235M
2025-03-10Seed· $200M
Recent News
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