Your Impact at LILA

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.

About The Team

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 fast.
• Your team ships things that matter. The tools they build accelerate research timelines from months to days.

If you want to lead at the intersection of AI and science, move fast, and grow the kind of team that can architect systems that don't exist yet — we want to talk.

What You'll Be Building

• Team Leadership: Build, mentor, and manage a high-performing team of 8-10 engineers spanning full-stack, agent, and platform expertise. Foster a culture of collaboration, ownership, and continuous improvement; conduct performance reviews, provide feedback, and identify opportunities for growth. Manage team workload, prioritize projects, and ensure timely delivery of high-quality solutions.
• Technical Strategy & Execution: Define and execute the technical roadmap for LILA's application layer aligning with LILA's broader AI and product strategy. Drive innovation in how scientists interact with AI-driven systems.
• Product Delivery: Own end-to-end delivery of the application platform — chat, agents, artifacts, and the integrations connecting them to lab workflows and ML pipelines — ensuring reliability, performance, and security across everything the team ships.
• Cross-Functional Collaboration: Partner with ML researchers, scientists, product managers, and other engineering leaders to understand scientific workflows and translate them into product capabilities. Communicate technical concepts effectively to both technical and non-technical audiences; manage expectations and ensure alignment across teams.
• Engineering Thought Leadership: Represent LILA's applied-AI work externally through conferences, presentations, and writing — helping attract top talent and establishing LILA's leadership at the intersection of AI and science.

What You'll Need To Succeed

• 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.
• Acute listening skills and a proven track record of partnering cross-functionally with scientists, ML engineers, product, and design; able to explain complex ideas to diverse audiences.

Bonus Points For

• Applied AI Engineering leadership: Experience shipping products built on AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks.
• AI-native product intuition: A point of view on what "good" looks like for chat, agent, and copilot experiences — and how to evolve it as the underlying models improve.
• Cloud & DevOps depth: Familiarity with AWS, Kubernetes, infrastructure-as-code (Terraform, CloudFormation), and CI/CD (GitHub Actions).
• Thought leadership in the applied-AI community via conference talks, blog posts, or open source.
• Scientific domain exposure: Experience with laboratory software, analytics for life sciences or material sciences, or adjacent scientific computing domains.