Associate Director, App

<p><strong>Your Impact at LILA</strong></p> <p>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.</p> <p>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.</p> <p><strong>About The Team</strong></p> <p>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.</p> <ul> <li><strong>AI isn't a feature here — it's the architecture.</strong> Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons.</li> <li><strong>The problems are genuinely hard.</strong> Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them.</li> <li><strong>You'll lead engineers who learn domains they never expected.</strong> Working shoulder-to-shoulder with lab scientists and ML engineers means your team's technical surface area grows fast.</li> <li><strong>Your team ships things that matter.</strong> The tools they build accelerate research timelines from months to days.</li> </ul> <p>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.</p> <p><strong>What You'll Be Building</strong></p> <ul> <li><strong>Team Leadership:</strong> 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.</li> <li><strong>Technical Strategy & Execution:</strong> 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.</li> <li><strong>Product Delivery:</strong> 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.</li> <li><strong>Cross-Functional Collaboration:</strong> 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.</li> <li><strong>Engineering Thought Leadership:</strong> 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.</li> </ul> <p><strong>What You'll Need To Succeed</strong></p> <ul> <li>Bachelor's or Master's degree in Computer Science, Engineering, or related field.</li> <li>12+ years of engineering experience building and deploying large-scale production systems, with 5+ years leading senior engineers.</li> <li>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.</li> <li>Deep technical judgment on Applied AI (agents, MCP, context engineering) and across the full stack (React, TypeScript, Python, FastAPI, SQL/NoSQL, AWS, Kubernetes).</li> <li>Hands-on experience — personally and on the teams you've led — using AI coding assistants and agentic tooling to drive productivity.</li> <li>Proven ability to define technical strategy, drive it to execution, and balance trade-offs between scalability, performance, delivery speed, and maintainability.</li> <li>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.</li> </ul> <p><strong>Bonus Points For</strong></p> <ul> <li><strong>Applied AI Engineering leadership:</strong> Experience shipping products built on AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks.</li> <li><strong>AI-native product intuition:</strong> 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.</li> <li><strong>Cloud & DevOps depth:</strong> Familiarity with AWS, Kubernetes, infrastructure-as-code (Terraform, CloudFormation), and CI/CD (GitHub Actions).</li> <li><strong>Thought leadership</strong> in the applied-AI community via conference talks, blog posts, or open source.</li> <li><strong>Scientific domain exposure:</strong> Experience with laboratory software, analytics for life sciences or material sciences, or adjace