Senior / Principal Scientist, AI for Protein Engineering

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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 (LSAI), the AI for Protein Engineering team develops and uses the generative and predictive models that drive Lila's biomolecule design programs from in silico hypothesis to wet-lab validated lead. We are seeking a Senior or Principal Scientist to join this team as a senior IC focused on antibody design and engineering. You will develop and execute the methods and workflows that ensure successful completion of antibody campaigns. Scope may expand to additional modalities such as enzymes and peptides as needs evolve. This role sits at the bilingual edge of ML and biology. You will own biological understanding of campaign needs and partner closely with the Life Science Research team to design and validate computational predictions in the lab. You will shape the technical agenda for AI protein engineering at Lila and represent that work both internally and to the broader research community. What You'll Be Building Develop and own protein design and engineering workflows for antibody campaigns, including de novo design, affinity maturation, and developability optimization Execute design workflows end-to-end for active campaigns and deliver wet-lab-validated leads against program milestones Translate campaign requirements - epitope selection, affinity targets, biophysical constraints, and developability criteria - into well-defined ML problems and design specifications Adapt and extend state-of-the-art AI methods (generative models, protein language models, structure-conditioned design) to the specific demands of antibody and broader biomolecule engineering Partner with the Life Science Research team on design validation, building active learning loops where wet-lab data refines and improves model performance Expand the protein engineering platform to additional modalities such as enzymes and peptides as needs evolve What You'll Need to Succeed PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, or a related quantitative field Proven track record of successful design of wet-lab-validated biomolecules through AI, with industry experience strongly preferred Deep ML expertise with the ability to modify and adapt state-of-the-art AI approaches for protein engineering, not just apply them off-the-shelf Strong fluency across both ML and protein biology, with hands-on understanding of antibody design Demonstrated ability to drive a research and engineering program independently, from problem definition through experimental validation and iteration Track record of close collaboration with experimental scientists and clear communication across the ML/biology boundary Bonus Points For Direct experience designing antibodies, nanobodies, or other therapeutic proteins for clinical or therapeutic pipelines Experience with structure prediction, generative protein design (diffusion, flow-matching, or similar), and protein language models in a production research setting Experience in structural biology and conformational dynamics Experience extending design methods to additional modalities such as enzymes, peptides, or other engineered biomolecules High-impact publications or open-source contributions in AI for Science (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent) Experience designing or operating active learning loops between computational design and high-throughput experimental validation

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