Staff ML Engineer, Life Sciences AI

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns.

We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack — pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables.

What You'll Be Building

• Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems.
• Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle — connecting computational outputs to experimental inputs and feeding results back into design workflows.
• Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns.
• Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems.
• Set engineering standards for LSAI software — design reviews, CI/CD, testing, observability, reproducibility — and mentor senior engineers as the team grows.
• Diagnose and resolve reliability, performance, and scaling bottlenecks in production pipelines supporting partnership deliverables.

What You’ll Need to Succeed

• Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience).
• 8+ years of professional software engineering experience in Python (or comparable systems languages).
• Proven experience designing, building, and operating scalable production systems — APIs, data pipelines, orchestration, and cloud infrastructure.
• Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices.
• Experience building or operating scientific or ML-adjacent infrastructure — workflow orchestration, experiment tracking, and reproducible pipelines.
• Hands-on experience with containerization, orchestration platforms, and infrastructure-as-code on a major cloud provider.
• Track record of leading technical direction across multiple systems and partnering deeply with research scientists or ML engineers to translate scientific needs into production engineering.

Bonus Points For

• Experience building infrastructure for protein design and engineering, antibody engineering, or other molecular ML applications.
• Familiarity with biological data formats and bioinformatics tooling.
• Experience integrating ML training/inference systems with broader product or scientific platforms.
• Open-source contributions to scientific computing or data infrastructure projects.