Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery

Lila Sciences
Cambridge, London, San Francisco
Workplace: OnsiteFull timeUSD 228,000 - 358,000 annuallyFunction: Research & Scientific (R&D)Education: phdSkills: ["Scientific judgment","Collaboration","Reasoning","Uncertainty estimation"]

Build data-efficient machine learning models for drug discovery in low-data, high-quality regimes. Develop active learning, meta-learning, fine-tuning, uncertainty estimation, and experimental design methods, and create multimodal models integrating DEL, simulation, assays, protein/structure, text signals, and metadata. Partner with experimental, computational, and drug discovery teams to create closed-loop learning workflows that continuously improve models and translate predictions into actionable acquisition recommendations.

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Lila Sciences
Lila Sciences
3 days ago

Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery

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Last checked: 21 hours agoStatus: Live

Job Summary

Build data-efficient machine learning models for drug discovery in low-data, high-quality regimes. Develop active learning, meta-learning, fine-tuning, uncertainty estimation, and experimental design methods, and create multimodal models integrating DEL, simulation, assays, protein/structure, text signals, and metadata. Partner with experimental, computational, and drug discovery teams to create closed-loop learning workflows that continuously improve models and translate predictions into actionable acquisition recommendations.
Location: Cambridge, London, San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Build ML models that work well in low-data regimes for drug discovery and molecular optimization.
  • •Design data acquisition strategies to decide which compounds, assays, DEL selections, simulations, predictions, or experiments to run next.
  • •Develop approaches including active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling for focused chemical spaces.
  • •Train multimodal models integrating DEL data, simulation outputs, assay data, protein/structural information, chemical features, and text-derived signals plus metadata.
  • •Develop closed-loop learning workflows and translate model predictions and uncertainty into recommendations for compound, assay, batch, and next-experiment selection.

Pay and Benefits

Salary: USD 228,000 - 358,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid ParentalCommuter BenefitsMeal Allowance

Key Requirements

  • •PhD (or equivalent experience) in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or a related field.
  • •Strong experience training ML models in low-data regimes.
  • •Experience with data-efficient learning methods such as active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, and uncertainty estimation.
  • •Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets.
  • •Practical experience with PyTorch, JAX, scikit-learn (or equivalent ML tools).
Experience:Drug discoveryComputational chemistryComputational biologyData-efficient learningMultimodal learning
Education:PhD / Doctorate
Skills:Scientific judgmentCollaborationReasoningUncertainty estimation
Languages:English
Tech Stack:PyTorchJAXScikit-learn

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
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