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About the Role

Lila Sciences is seeking a Machine Learning Scientist, Data-Efficient Learning for Drug Discovery to build models and learning strategies for settings where data is scarce, expensive, and intentionally generated. This role is focused on training useful models from low-quantity but high-quality datasets and deciding what data should be acquired next. This is an applied scientific ML role in a frontier research area, requiring the use and development of approaches across active learning, meta-learning, fine-tuning, uncertainty estimation, experimental design, and multimodal modeling to help Lila build closed-loop systems that learn efficiently from targeted data acquisition. This role connects model training with scientific decision-making.

Responsibilities

Build ML models that perform well in low-data regimes for drug discovery and molecular optimization.
Design data acquisition strategies that identify which compounds, assays, DEL selections, simulations, structural predictions, or experiments should be run next to maximize learning.
Develop active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces.
Train models on low-quantity, high-quality datasets generated by Lila's experimental, computational, and agentic discovery systems.
Build multimodal models that can integrate DEL data, simulation outputs, assay data, protein and structural information, chemical features, literature or text-derived signals, images, and experimental metadata.
Partner with experimental, computational, and drug discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible.
Evaluate models through learning curves, prospective validation, retrospective benchmarks, uncertainty calibration, and decision-focused metrics.
Develop closed-loop learning workflows that continuously update models as new data arrives from experiments, simulations, and automated systems.
Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, batch design, or next experiments.
Work with platform and agent teams to expose model-driven recommendations as tools for scientists and AI agents.

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 active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, uncertainty estimation, or related data-efficient learning methods.
Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets.
Experience with multimodal learning or methods that combine heterogeneous data sources.
Ability to reason about data acquisition strategy, not only model fitting.
Strong scientific judgment and ability to connect model behavior to experimental decisions.
Practical experience with PyTorch, JAX, scikit-learn, or equivalent ML tools.
Ability to collaborate across ML, data, computational science, experimental, and drug discovery teams.

Skills

Active learning
Meta-learning
Fine-tuning
Uncertainty estimation
Experimental design
Multimodal modeling
Bayesian optimization
Transfer learning
PyTorch
JAX
scikit-learn

Location

U.S.

Work Type

Full-time

Experience Level

PhD or equivalent experience

Education Level

Salary/Compensations

$228,000—$358,000 USD

Benefits

Competitive base compensation with bonus potential and generous early-stage equity.
Medical, dental, and vision coverage.
Employer-paid life and disability insurance.
Flexible time off with generous company wide holidays.
Paid parental leave.
Educational assistance program.
Commuter benefits, including bike share memberships for office based employees.
Company subsidized lunch program.
International benefits tailored to their region.

About the Company

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges.
We believe science is the most inspiring frontier for AI.
Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance.

Equal Opportunity

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.
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