Scientist II / Senior ML Scientist, Cofolding and Structure-Aware ML
London, England, United Kingdom · On-site
$228k–$358k/yr
Senior$551M raised
Your Impact at LILA Lila Sciences is seeking a Machine Learning Scientist, Cofolding and Structure-Aware ML to train next-generation cofolding models for drug discovery. This role is focused on improving models that reas…
Skills: Machine learning, Deep learning, Cofolding, Protein-ligand modeling, Representation learning
Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery
London, England, United Kingdom · On-site
$228k–$358k/yr
Senior$551M raised
Your Impact at LILA 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 intentionall…
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Scientist II/Senior Scientist, Computational Chemistry, Drug Discovery
London, England, United Kingdom · On-site
$141k–$218k/yr
Senior$551M raised
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Principal / Senior Principal Scientist, Small Molecule Therapeutics
Cambridge, Massachusetts, United States · On-site
Senior+$551M raised
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Senior/Principal Scientist, Small Molecule Therapeutics
Cambridge, Massachusetts, United States · On-site
$148k–$208k/yr
Senior+$551M raised
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Cambridge, Massachusetts, United States · On-site
$204k–$346k/yr
Senior+$551M raised
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Associate Scientist/Scientist I, Protein Science Developability
Cambridge, Massachusetts, United States · On-site
$88k–$144k/yr
Mid level$551M raised
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Operations Graduate Program – Summer 2027 Internship
Thousand Oaks, California, United States · On-site
$35/hr–$41/hr
Entry level$29B raised
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$228k–$358k/yr
Full-time
postgraduate degree
Medical coverage, Dental coverage, Vision coverage, Employer-paid life insurance, Disability insurance, Flexible time off
Posted 10d ago
~40 hrs/week
Responsibilities
The scientist will develop and train next-generation cofolding models to advance drug discovery by integrating protein, ligand, and experimental data. They will collaborate with cross-functional teams to build rigorous evaluation frameworks and scale model training workflows.
Requirements
Candidates must hold a PhD or equivalent experience in machine learning, computational biology, or a related field. Hands-on experience with deep learning for scientific applications and proficiency in frameworks like PyTorch or JAX are required.
Full job description
Your Impact at LILA
Lila Sciences is seeking a Machine Learning Scientist, Cofolding and Structure-Aware ML 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.
This person should have direct experience training modern scientific ML models, not only using pretrained systems. 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.
The models developed in this role should produce outputs that medicinal and computational chemists as well as biophysicists can interrogate, validate, and use in downstream agent-driven discovery decisions.
What You'll Be Building
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.
What You'll Need to Succeed
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.
Bonus Points For
Hands-on experience with DEL data.
Drug discovery experience, especially in protein-ligand modeling or molecular optimization contexts.
Experience with Boltz, AlphaFold or AlphaFold-derived methods, equivariant GNNs, diffusion models, protein language models, or molecular encoders.
Experience training or extending cofolding, protein-ligand, protein-protein, structure prediction, diffusion, or geometric deep learning models.
Experience with distributed model training and large-scale scientific data pipelines.
Familiarity with active learning or closed-loop molecular design.
Experience integrating ML models into agentic scientific workflows.
Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$228,000—$358,000 USD
About LILA
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. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We’re All In
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.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
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 building the foundation to apply AI to every aspect of the scientific method, enabling scientists to bring forth solutions in human health and sustainability at a pace and scale never experienced before.
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