ML Scientist: Protein Design & Autonomous Discovery

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

Benefits offered by this job

Medical, dental, vision

Employer-paid life and disability

Flexible time off

Parental leave

Educational assistance

Commuter benefits

Lunch program

Job summary

Lila Sciences is building an autonomous science platform to co-evolve AI and lab experimentation for protein design. We seek a science-minded ML engineer to design molecules on real programs and translate results into scalable capabilities across projects.

The role combines protein sequence/structure expertise with software engineering to create reliable, extensible systems that improve molecule design decisions across programs.

Qualifications

• MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or similar quantitative field.
• Strong software engineering and system design fundamentals.
• Rigor in evaluation and dataset design: how benchmarks leak, why a validation number may fail downstream, and how to measure automated decision quality.
• Strong cross-functional communication skills.
• Domain expertise in protein sequence, structure, and function.

Responsibilities

• Design molecules for active biologics programs, translating targets and constraints into actionable design hypotheses.
• Develop reasoning capabilities for drug discovery, including orchestrating design workflows.
• Design and maintain benchmarks and evaluation infrastructure to measure generalizable design decisions across biologics programs.
• Own operations around reproducibility, throughput, and inference cost of computational design workflows.
• Collaborate with domain scientists to align ML objectives with design priorities.

Job description

Lila Sciences is building an autonomous science platform to co-evolve AI and lab experimentation for protein design. We seek a science-minded ML engineer to design molecules on real programs and translate results into scalable capabilities across projects.

The role combines protein sequence/structure expertise with software engineering to create reliable, extensible systems that improve molecule design decisions across programs.