ML Scientist I/II, AI for Protein Engineering
$176k - $304k • San Francisco, CA USA • Full-time
Posted 1mo ago
Job Location
San Francisco, CA USA
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila Sciences is developing a platform where AI and automation work together to tackle complex problems in medicine. The AI for Protein Engineering team focuses on creating and using generative and predictive models to advance biomolecule design programs from initial concepts to validated leads. This role is at the intersection of machine learning, protein engineering, and therapeutic design, requiring strong ML fundamentals, a curiosity for protein biology, and an interest in computationally designed, wet-lab-validated biologics. You will collaborate with experimental scientists, AI researchers, and platform teams to build models and workflows that support Lila’s autonomous science platform.
Responsibilities
- Build ML workflows for protein engineering campaigns, from design specification through experimental learning.
- Develop and adapt methods for de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning.
- Integrate protein design methods into robust software systems and broader reasoning models.
- Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans.
- Partner with experimental scientists to interpret biomolecule success/failure and translate insights into model improvements.
- Build evaluation frameworks for model generalization to challenging biologics design problems.
Requirements
- PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
- Experience applying machine learning to protein design, biologics engineering, or related biomolecular design problems.
- Strong ML fundamentals with hands-on experience developing, adapting, training, or evaluating modern AI methods.
- Fluency with biological sequence, structure, function, developability, or experimental validation considerations.
- Ability to translate therapeutic or biological objectives into computational design problems and model evaluation plans.
- Strong collaboration and communication skills across ML, biology, experimental science, and software teams.
Benefits
- Competitive base compensation with bonus potential
- 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
- Company subsidized lunch program