lilasciences · San Francisco, CA USA
Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team builds models and systems that take a biologic from design specification to wet-lab validated lead. We're hiring a machine learning scientist to design molecules on real programs and turn what they learn into capabilities that generalize across programs. The ideal candidate is an exceptional builder with strong biological intuition: someone who can turn work on individual campaigns into reliable, extensible systems that improve how we design molecules across programs. You'll work closely with domain scientists, platform teams, and AI researchers to connect specialist protein design models to Lila's broader autonomous science platform. What You'll Be Building Design molecules for active biologics programs, partnering with domain scientists to translate target, mechanism, and experimental constraints into actionable design hypotheses. Develop reasoning capabilities for drug discovery, including orchestrating design workflows. Design and maintain benchmarks and evaluation infrastructure that measure whether design workflows produce useful, generalizable decisions across biologics programs. Own operations around reproducibility, throughput, and inference cost of computational design workflows. Work with domain scientists to understand how designs are prioritized and turn that judgment into ML objectives and evaluation criteria. What You'll Need to Succeed MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or a similar quantitative field. Strong software engineering and system design fundamentals. Rigor in evaluation and dataset design: how benchmarks leak, why a good validation number fails downstream, and how to measure whether an automated system is making good decisions. Strong cross-functional communication skills. Domain expertise in…
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