Director / Senior Director, Research Engineering, Life Sciences AI

About the role

Building the Core for Life Sciences AI at Lila Sciences

Lila Sciences is constructing a new era of discovery, powered by advanced computation. At the heart of this mission is the Life Sciences Artificial Intelligence (LSAI) organization, which is architecting the computational engine that drives our scientific breakthroughs. We are seeking a Director or Senior Director of Research Engineering to own the foundational platform that our researchers and models depend upon. This is a pivotal role that sits at the intersection of core systems development and team leadership. You will not only define the technical bedrock but also guide a growing engineering organization, ensuring our scientists can move with speed and confidence. Innovation here is built on a platform of reliability, clarity, and deep scientific insight.

This position is a true integration of individual contribution and expanding influence. You will be the primary architect of the LSAI codebase, making high-stakes decisions that determine how complex scientific tools are built and delivered. As the team scales, your leadership will be essential in setting a standard for quality and collaboration. The role demands a rare blend of deep technical execution and the strategic vision to build infrastructure that stands the test of rigorous research. Your work will directly determine the velocity and robustness of the science that emerges from our labs.

What You Will Build

Your primary mission is to design, implement, and safeguard the central infrastructure that powers Lila's scientific endeavors. You will translate the needs of biologists and researchers into resilient, scalable software foundations. This involves creating the critical pathways that allow complex data to flow into powerful models. Your decisions will shape the developer experience for everyone building on the platform.

• Architecting the central platform that transforms diverse and unstructured biological data into reliable, model-ready inputs.
• Defining and enforcing rigorous engineering standards for testing, version control, and documentation to ensure reproducibility.
• Making key architectural choices that honor the need for scientific agility without compromising engineering integrity.
• Proactively identifying infrastructure constraints and plotting a course for platform evolution that supports ambitious research goals.
• Building and nurturing an engineering team, balancing hands-on coding with mentorship and technical guidance.
• Serving as a crucial liaison, architecting APIs and interfaces that connect our core platform with external tools and model-building workflows.
• Driving performance initiatives that reduce training and inference times across our GPU and compute clusters.
• Developing low-level optimizations, including CUDA and Triton kernels, to maximize the efficiency of our hardware.

What You Will Need

Success in this role requires a proven ability to build the complex systems that underpin modern scientific discovery. You must be comfortable operating at the highest levels of software engineering while maintaining a deep connection to the scientific problems we are solving. Your background should demonstrate a commitment to delivering production-grade systems that are used by demanding researchers.

• A history of designing and delivering core platforms or frameworks that are essential to engineering and scientific teams.
• Mastery of platform architecture, with a portfolio that demonstrates deployment into high-stakes environments.
• Extensive experience managing the full machine learning lifecycle, from initial experiments through robust production deployment.
• The ability to fluidly switch between strategic platform planning and deep, hands-on implementation challenges.
• A track record of mentoring engineers and establishing technical practices that promote long-term quality and team cohesion.

Additional Strengths We Value

While not required, these experiences will significantly amplify your impact from day one. They represent the kind of nuanced understanding that allows for seamless collaboration within a dynamic research environment.

• Prior work in bioML labs or high-performance scientific computing settings.
• A working knowledge of the challenges and constraints in computational biology and related fields.
• The ability to work effectively alongside model specialists, focusing on the plumbing and tools they rely on.
• Expertise in performance engineering, including profiling and optimization of large-scale training and inference jobs.
• Deep familiarity with the modern machine learning stack, including PyTorch internals and distributed training across complex clusters.

What you'll do

• Meet the bar Practical notes