LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineering leaders who want to build the team that solves problems no one has solved before. We're hiring an Associate Director of Engineering, Application Team to lead the engineers designing the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI.
What they're looking for
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 12+ years of engineering experience building and deploying large-scale production systems, with 5+ years leading senior engineers
- Track record of building and growing high-performing engineering teams in high-growth or early-stage environments where speed-to-value mattered as much as long-term architecture
- Deep technical judgment on Applied AI (agents, MCP, context engineering) and across the full stack (React, TypeScript, Python, FastAPI, SQL/NoSQL, AWS, Kubernetes)
- Hands-on experience — personally and on the teams you've led — using AI coding assistants and agentic tooling to drive productivity
- Proven ability to define technical strategy, drive it to execution, and balance trade-offs between scalability, performance, delivery speed, and maintainability
More about this role
Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineering leaders who want to build the team that solves problems no one has solved before.
We're hiring an Associate Director of Engineering, Application Team to lead the engineers designing the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI.
The Application Team sits at the center of LILA — the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software become one AI-native experience that carries a scientist from hypothesis to experiment to breakthrough results.
- AI isn't a feature here — it's the architecture. Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons.
- The problems are genuinely hard. Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them.
- You'll lead engineers who learn domains they never expected. Working shoulder-to-shoulder with lab scientists and ML engineers means your team's technical surface area grows...
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