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Salary: USD 320,000 - 490,000 / annual
Lila Sciences is seeking a Director of Research Engineering to lead the Life Sciences AI (LSAI) platform team. This role combines significant hands-on technical contribution with growing management responsibility, reporting to the SVP of Generative Biology.
You will architect and build the core computational platform that powers Lila's life sciences work—the foundation on which scientists build and deploy their models. Key responsibilities include designing the LSAI codebase architecture, establishing engineering standards for code quality, testing, versioning, and documentation, and making architectural decisions that balance rigor with the reality that most contributors are scientists first, engineers second.
You'll anticipate infrastructure bottlenecks, define the platform roadmap to enable rapid iteration while maintaining quality and reproducibility, and build and manage an engineering team as headcount grows. IC contribution remains the top priority; you'll remain a primary hands-on contributor while scaling leadership.
Required qualifications include a strong track record designing and building core software platforms or frameworks that scientists and engineers depend on, deep platform architecture expertise, experience building infrastructure alongside scientists in research environments without sacrificing velocity, and full ML lifecycle expertise across data, training, evaluation, and MLOps. You should be comfortable shifting between hands-on building and strategic leadership, and have experience mentoring people and setting technical practices across teams.
Bonus qualifications include bioML lab or scientific computing experience, familiarity with computational biology or protein modeling, performance engineering expertise, CUDA/Triton kernel experience, and deep expertise in the modern ML systems stack including PyTorch internals, mixed precision, and distributed training.
Lila Sciences is building Scientific Superintelligence to solve major challenges by combining advanced AI models with proprietary instruments into an operating system for science that executes the scientific method autonomously, accelerating discovery across medicine, materials, and energy.
