ML Scientist, Foundation Models for Life Sciences
$176k - $304k • San Francisco, CA USA • Full-time
Posted 6mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
San Francisco, CA USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila Sciences is building a platform where AI and automation co-evolve to solve complex problems in medicine. The Foundation Models team within Life Science AI (LSAI) focuses on developing foundation models that integrate biological sequence, molecular structure, and experimental data to drive automated scientific discovery. This role involves contributing to problem formulation, model design, training, evaluation, and integration into Lila's discovery engine, with a specific emphasis on structure prediction and co-folding for antibody and biologics design. The position is for an individual contributor focused on building deep expertise in structure-aware generative AI for biology, owning research sub-problems, and collaborating with experimental scientists to close the computational-experimental loop.
Responsibilities
- Train and evaluate structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems.
- Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods.
- Develop rigorous evaluation frameworks to ensure model generalization for de novo design problems.
- Scale training, inference, and evaluation workflows across large GPU clusters.
- Participate in the end-to-end ML process within Lila's 'Lab-in-the-Loop' lifecycle, including shaping data generation strategy, building pipeline models, and designing feedback loops.
- Contribute to adjacent foundation model research, such as biological sequence design and multimodal scientific reasoning.
- Translate biological questions into well-defined ML problems and interpret model outputs with scientific collaborators.
- Support research quality and methodology standards within the foundation models program.
Requirements
- PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field, or Master's with equivalent research experience.
- Hands-on experience training deep learning models on molecular, protein, or structural data.
- Strong foundation in generative model architectures and training, with demonstrated ability to design experiments, ablations, and evaluations.
- Ability to formulate and execute research independently.
- Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related).
- Experience collaborating with experimental scientists or working with biological/chemical data.
- Proficiency in ML frameworks like PyTorch, JAX, or TensorFlow.
- Experience with GPU-based training workflows.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Comprehensive 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
- Company-subsidized lunch program