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Salary: USD 176,000 - 304,000 / annual
Lila Sciences is building an autonomous science platform that combines advanced AI with proprietary instruments to execute the scientific method at scale. The AI for Protein Engineering team is seeking a Machine Learning Scientist to design molecules for active biologics programs and develop generalizable capabilities across multiple programs.
You will work at the intersection of machine learning and protein design, partnering with domain scientists, platform teams, and AI researchers. Your responsibilities include:
• Design molecules for active biologics programs by translating target specifications, mechanisms, and experimental constraints into actionable design hypotheses
• Develop reasoning capabilities and orchestration systems for drug discovery workflows
• Design and maintain benchmarks and evaluation infrastructure to measure whether design workflows produce useful, generalizable decisions across programs
• Own operations around reproducibility, throughput, and inference cost of computational design workflows
• Work with domain scientists to understand design prioritization and translate that judgment into ML objectives and evaluation criteria
You'll be an exceptional builder with strong biological intuition who can turn work on individual campaigns into reliable, extensible systems. The role requires close collaboration across functions and the ability to connect specialist protein design models to the broader autonomous science platform.
REQUIREMENTS:
• 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: understanding how benchmarks leak, why validation metrics fail downstream, and how to measure whether automated systems make good decisions
• Strong cross-functional communication skills
• Domain expertise in protein sequence, structure, and function
BONUS QUALIFICATIONS:
• Experience building reasoning models, agents, planning systems, or multi-step ML orchestration
• Exposure to designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn loops
• Experience developing evaluation harnesses, model registries, or benchmark suites
• Training or serving models at scale (distributed training, GPU efficiency, high-throughput inference)
• Publications, open-source contributions, or applied research outputs in AI for Science venues
