Director / Senior Director, Research Engineering, Life Sciences AI
$320,000–$490,000 year
On-siteSan Francisco, California, United States
Job Summary
Design and own the core LSAI codebase infrastructure that scientist-owned models plug into, setting engineering standards for code quality, testing, and versioning. Make architectural decisions balancing rigor with the reality that contributors are scientists first, while anticipating bottlenecks to define a platform roadmap enabling rapid iteration. Build and manage an engineering team over time while maintaining primary hands-on contribution to the ML lifecycle across data, training, evaluation, and MLOps. This hybrid role combines deep IC work with growing management responsibility, reporting to the SVP of Generative Biology within the LSAI leadership team. Candidates must balance strategic leadership with individual output, mentoring people and establishing technical practices. The position requires full ML lifecycle expertise and comfort shifting between building and strategy without letting either crowd out the other.
Required Qualifications
- Strong track record designing and building core software platforms or frameworks that scientists and engineers depend on
- Full ML lifecycle expertise across data, training, evaluation, and MLOps, with a track record of taking research code to production
- Experience mentoring people and setting technical practices across a team or organization, beyond individual output
- Comfort shifting between hands-on building and strategic leadership without letting either crowd out the other
Desired Qualifications
- 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
- 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
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