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LILA is building an AI-native platform that enables scientists to move seamlessly from hypothesis to experimental results without context-switching between tools. The Associate Director of Application Engineering will lead the team responsible for the application layer—chat interfaces, AI agents, artifacts, and integrations connecting to lab workflows and ML pipelines.
You will manage and mentor a team of 8–10 engineers spanning full-stack, agent, and platform expertise. Responsibilities include defining and executing the technical roadmap for LILA's application layer, ensuring alignment with broader AI and product strategy, and owning end-to-end delivery of reliable, performant, and secure systems. You'll conduct performance reviews, manage workload prioritization, and foster a culture of ownership and continuous improvement.
The role sits at the intersection of AI and science. You'll partner closely with ML researchers, scientists, product managers, and other engineering leaders to translate scientific workflows into product capabilities. You'll also represent LILA's applied-AI work externally through conferences, presentations, and writing to attract talent and establish thought leadership.
The Application Team is the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software converge. AI is the architecture here—agent frameworks, LLM orchestration, and tool protocols are core primitives, not add-ons. Problems are genuinely hard: connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs requires inventing new patterns.
Required: 12+ years of engineering experience building large-scale production systems, with 5+ years leading senior engineers. Bachelor's or Master's in Computer Science or related field. Deep technical judgment on applied AI (agents, MCP, context engineering) and full-stack capabilities (React, TypeScript, Python, FastAPI, SQL/NoSQL, AWS, Kubernetes). Proven ability to define technical strategy, drive execution, and balance scalability, performance, speed, and maintainability. Strong cross-functional collaboration skills and ability to explain complex ideas to diverse audiences.
Bonus: Experience shipping AI agent products, RAG pipelines, or LLM orchestration frameworks; intuition for chat/agent/copilot UX; AWS/Kubernetes/infrastructure-as-code depth; thought leadership in applied AI; scientific domain exposure.
