Senior ML Scientist, AI for Protein Engineering
$268k - $358k • 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 collaborate to address complex challenges in medicine, specifically within Life Sciences AI. The AI for Protein Engineering team focuses on creating and implementing generative and predictive models to advance biomolecule design programs from initial concepts to validated leads. This role is for a senior individual contributor specializing in computational biologics design, working on active protein engineering projects and enhancing Lila's capabilities in designing, evaluating, and learning from biomolecular data. The position is at the nexus of machine learning, protein engineering, and therapeutic design, requiring deep ML expertise, a strong understanding of protein biology, and a proven ability to deliver computationally designed, validated biologics.
Responsibilities
- Own applied ML workflows for protein engineering campaigns, from design specification through experimental learning.
- Develop and adapt methods for de novo generation, sequence/structure-based property prediction, candidate selection, and active learning.
- Integrate ML methods into robust software systems and broader reasoning models.
- Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans.
- Collaborate with experimental scientists to interpret biomolecule success/failure and refine models and design principles.
- Build rigorous evaluation frameworks for model generalization in biologics design.
Requirements
- PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
- Strong track record applying machine learning to protein design, biologics engineering, or related biomolecular design problems.
- Industry experience in biomolecular design is strongly preferred.
- Deep ML expertise with hands-on experience adapting and developing modern AI methods.
- Strong intuition for therapeutic biologics design, including sequence, structure, function, developability, and experimental validation.
- Demonstrated ability to independently drive applied research from problem definition to experimental validation and iteration.
- Strong collaboration and communication skills across ML, biology, experimental science, and software teams.
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 for office-based employees)
- Company-subsidized lunch program