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About the Role

Lila Sciences is seeking a Machine Learning Scientist to train next-generation cofolding models for drug discovery. This role is focused on improving models that reason over proteins, ligands, binding context, and experimental data, potentially using contrastive learning and related representation-learning approaches. You will work with ML researchers, computational chemists, computational biophysicists, data engineers, and drug discovery teams to develop models that learn from DEL and related datasets, connect molecular and protein context, and improve AI-driven discovery decisions.

Responsibilities

Train and evaluate cofolding models for protein-ligand and related molecular discovery applications.
Use contrastive learning, representation learning, self-supervised learning, or related methods where they help improve cofolding models trained on molecules, proteins, structures, and experimental readouts.
Develop modeling approaches that make DEL data more useful for learning binding, enrichment, selectivity, and structure-activity signals.
Build and evaluate models informed by Boltz, AlphaFold-style cofolding, equivariant GNNs, and related structure-aware ML methods.
Design training objectives, including contrastive, self-supervised, or multimodal objectives, that connect ligands, proteins, structures, assays, simulations, and experimental data.
Build rigorous evaluation frameworks that distinguish meaningful molecular learning from dataset artifacts, leakage, or spurious correlations.
Collaborate with data and platform teams to define datasets, labels, negatives, controls, and metadata needed for model training.
Partner with computational chemistry and biophysics teams to connect model outputs to physically and chemically meaningful hypotheses.
Work with low-data learning scientists to identify which DEL, assay, simulation, or structural data would most improve model performance in focused chemical spaces.
Work with research engineers to scale training, inference, and evaluation workflows.
Help expose trained models and model-derived capabilities as tools for scientists and AI agents.

Requirements

PhD or equivalent experience in machine learning, computational biology, computational chemistry, bioinformatics, computer science, or a related field.
Hands-on experience training deep learning models for molecular, protein, structural biology, or scientific data applications.
Experience with contrastive learning, representation learning, self-supervised learning, or multimodal learning.
Familiarity with DEL or related selection, enrichment, screening, or molecular assay datasets.
Experience with protein-ligand modeling, cofolding, structure prediction, geometric deep learning, or structure-aware molecular ML.
Practical experience with PyTorch, JAX, or an equivalent ML framework.
Ability to design careful experiments, ablations, and evaluations for scientific ML models.
Strong understanding of data quality, leakage risks, negative construction, and benchmark design.
Ability to collaborate across ML, data, computational science, and drug discovery functions.

Skills

Machine Learning
Cofolding
Structure-Aware ML
Protein-Ligand Modeling
Contrastive Learning
Representation Learning
Self-Supervised Learning
PyTorch
JAX
Deep Learning
Computational Biology
Computational Chemistry
Bioinformatics
Computer Science
DEL Data
Structure Prediction
Geometric Deep Learning
Equivariant GNNs
Diffusion Models
Protein Language Models
Molecular Encoders
Distributed Model Training
Active Learning
Closed-Loop Molecular Design

Location

U.S.

Work Type

Full-time

Experience Level

PhD or equivalent experience

Education Level

Salary/Compensations

$228,000—$358,000 USD

Benefits

Medical coverage
Dental coverage
Vision coverage
Employer-paid life insurance
Employer-paid disability insurance
Flexible time off
Paid parental leave
Educational assistance program
Commuter benefits
Company subsidized lunch program

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.