Teams at Lila Sciences

Build and lead enterprise platform infrastructure for multi-tenancy, identity, authorization, tenant isolation, metering, billing, audit logging, compliance, SDKs, and AI-native APIs. Architect scalable cloud-native systems for scientific data and workflows while coding hands-on in Python and influencing platform, infrastructure, security, AI/ML, and scientific application teams.

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.