Director / Senior Director, Research Engineering, Life Sciences AI

Your Impact at LILA

LSAI (Life Sciences Artificial Intelligence) is building the computational platform that powers Lila's work in the life sciences. We are looking for a Research Engineer to own and build the core codebase that our models plug into, the foundation on which our scientists build and ship their work. This role is part of the LSAI leadership team and reports to the SVP of Generative Biology.

This role combines deep hands-on IC work with growing management responsibility. You will personally architect and build the core platform while also building and leading a team as headcount grows. IC contribution remains the top priority for this role.

What You'll Be Building

• Design, build, and own the foundation of the LSAI codebase, the core infrastructure that scientist-owned models plug into.
• Set and enforce engineering standards for code quality, testing, versioning, documentation, and repository structure.
• Make architectural decisions that balance rigor with the reality that most contributors are scientists first, engineers second.
• Anticipate infrastructure bottlenecks and define the platform roadmap to enable rapid iteration while maintaining quality and reproducibility.
• Build and manage an engineering team over time while remaining a primary hands-on contributor.

What You'll Need to Succeed

• Strong track record designing and building core software platforms or frameworks that scientists and engineers depend on.
• Deep platform architecture expertise, with biological applications such as protein design, nucleic-acid design, or cell foundation models as a plus.
• Experience building infrastructure and tooling alongside scientists in a research environment without sacrificing velocity.
• Full ML lifecycle expertise across data, training, evaluation, and MLOps, with a track record of taking research code to production.
• Comfort shifting between hands-on building and strategic leadership without letting either crowd out the other.
• Experience mentoring people and setting technical practices across a team or organization, beyond individual output.

Bonus Points For

• Experience in a bioML lab or scientific computing environment.
• Familiarity with or curiosity about computational biology, protein modeling, or ML-adjacent codebases.
• Comfort working around model builders, even if you do not build the models yourself.
• Performance engineering experience, including profiling and optimizing training and inference.
• Experience writing or tuning CUDA or Triton kernels.
• Ability to reason about GPU utilization, MFU/HFU, memory bandwidth, and kernel-level bottlenecks.
• Deep expertise in the modern ML systems stack, including PyTorch internals, mixed precision, and distributed training across multi-GPU or multi-node clusters.