Protein Design ML Scientist - AI for Therapeutics

Lila Sciences; San Francisco (CA); On-site; USD 176,000 - 304,000; Full time

Benefits offered by this job

Medical/dental/vision

Life and disability insurance

Generous holidays and parental leave

Educational assistance

Commuter benefits

Lunch program

Job summary

Lila Sciences is seeking an ML Scientist I/II to advance AI-powered protein engineering. The role blends machine learning with biomolecular design, working with experimental scientists and platform teams to build models and workflows that accelerate discovery of therapeutic biologics.

You will contribute to de novo generation, sequence- and structure-based predictions, and active learning, translating biological questions into computable ML problems while supporting robust software systems and

Qualifications

• PhD in Computational Biology, CS, ML, Biophysics, Bioengineering, or related quantitative field.
• Experience applying ML to protein design or related biomolecular design problems.
• Hands-on experience developing, adapting, training, or evaluating modern AI methods.

Responsibilities

• Build ML workflows for protein engineering campaigns from design to experimental learning.
• Develop methods for de novo generation, sequence/structure-based property prediction, candidate selection, active learning.
• Integrate protein design methods into robust software systems and reasoning models.
• Translate therapeutic/biological questions into defined ML problems and evaluation plans.
• Collaborate with experimental scientists to interpret results and improve models.
• Create evaluation frameworks for model generalization to challenging biologics design problems.

Skills

PhD required

ML for protein design

Strong ML fundamentals

Biological sequence/structure fluency

Computational design translation

Cross-functional collaboration

Job description

Lila Sciences is seeking an ML Scientist I/II to advance AI-powered protein engineering. The role blends machine learning with biomolecular design, working with experimental scientists and platform teams to build models and workflows that accelerate discovery of therapeutic biologics.

You will contribute to de novo generation, sequence- and structure-based predictions, and active learning, translating biological questions into computable ML problems while supporting robust software systems and