Teams at Lila Sciences

Develop and apply machine learning methods for protein engineering, including generative design, property prediction, candidate selection, active learning, and sequence or structure modeling. Collaborate with experimental scientists to translate biological objectives into computational problems, evaluate model generalization, interpret wet-lab results, and improve design-test-learn workflows for therapeutic biomolecules.

Develop BioML systems that encode and evaluate mechanistic biological reasoning. Analyze single-cell, perturbation, proteomics, imaging, and genetic data; design experimental campaigns; build outcome-verifiable benchmarks; and connect computational predictions with experimental decisions. The role requires rigorous evidence assessment, uncertainty interpretation, reproducible analysis, and collaboration with ML engineers, experimental scientists, and platform teams.

Design therapeutic proteins for active biologics programs, translating biological constraints into machine learning hypotheses. Build reasoning and orchestration systems for drug discovery, develop benchmarks and evaluation infrastructure, and improve reproducibility, throughput, and inference efficiency. Collaborate with domain scientists and AI researchers to convert expert judgment into ML objectives and scalable platform capabilities. The role requires strong software engineering, evaluation rigor, biological intuition, and expertise in protein sequence, structure, and function.