ML Scientist I/II, AI for Protein Engineering

About the role

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 develops and applies generative and predictive models that move biomolecule design programs from in silico hypothesis to wet-lab validated leads. We are looking for an ML Scientist I/II focused on AI for protein engineering. The work spans active protein engineering programs and focused technology development that improves how Lila designs, evaluates, and learns from biomolecular sequence, structure, and function data. This role sits at the intersection of machine learning, protein engineering, and therapeutic design. The ideal candidate brings strong ML fundamentals, curiosity about protein biology, and interest in computationally designed, wet-lab-validated biologics. You’ll collaborate with experimental scientists, AI researchers, and platform teams to build models and workflows that support Lila’s broader autonomous science platform.

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

Responsibilities

• Build ML workflows for protein engineering campaigns, from design specification through experimental learning.
• Develop and adapt methods spanning de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning.
• Integrate protein design methods into robust software systems and broader reasoning models.
• Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans.
• Partner with experimental scientists to interpret why designed biomolecules succeed or fail, then turn those insights into model improvements.
• Build evaluation frameworks for model generalization to challenging biologics design problems.

Requirements

• PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
• Experience applying machine learning to protein design, biologics engineering, or related biomolecular design problems.
• Strong ML fundamentals, with hands-on experience developing, adapting, training, or evaluating modern AI methods.
• Fluency with biological sequence, structure, function, developability