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Salary: USD 268,000 - 358,000 / annual
Lila Sciences is building Scientific Superintelligence to solve major challenges in medicine, materials, and energy. The company combines advanced AI models with proprietary instruments into an operating system for science that executes the scientific method autonomously.
The AI for Protein Engineering team develops and applies generative and predictive models to move biomolecule design programs from computational hypothesis to wet-lab validated leads. You will be a senior individual contributor focused on computational biologics design, working at the intersection of machine learning, protein engineering, and therapeutic design.
Key responsibilities:
• Own applied ML workflows for protein engineering campaigns, from design specification through experimental learning and iteration.
• Develop and adapt methods spanning de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning. Integrate these 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 insights into better models and design principles.
• Build rigorous evaluation frameworks for model generalization to challenging biologics design problems.
You will collaborate with experimental scientists, AI researchers, and platform teams to connect specialist protein design models into Lila's broader autonomous science platform.
Requirements:
• PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or related quantitative field.
• Strong track record applying machine learning to protein design, biologics engineering, or related biomolecular design problems; industry experience strongly preferred.
• Deep ML expertise with hands-on experience adapting and developing modern AI methods, not just applying off-the-shelf approaches.
• Strong intuition for therapeutic biologics design, including sequence, structure, function, developability, and experimental validation considerations.
• Demonstrated ability to drive applied research independently, from problem definition through experimental validation and iteration.
• Strong collaboration and communication skills across ML, biology, experimental science, and software teams.
Bonus qualifications:
• Direct experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins for applied or clinical pipelines.
• Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models in production research.
• Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or biophysical constraints.
• Experience closing design-test-learn loops with wet-lab teams, including experimental prioritization, high-throughput validation, and active learning.
• Publications, open-source contributions, or applied research outputs in AI for science venues.
