Product Lead, Software/Applied AI

Overview of the role: As the Product Manager for Software / Applied AI at Lila, you will help build the first truly AI-native system of record for scientific discovery. We are looking for product leaders who can dive deep into a specific domain and own it completely. Whether your strengths align best with our core software platform or our cutting-edge agentic science offerings, you will be embedded with a dedicated scrum team to solve complex, full-stack challenges. You will sit at the intersection of commercial, engineering, design, and scientific research, building the tools that accelerate the next generation of scientific discovery. We are hiring for a wide range of function areas, and seniority levels — final title and level will be decided at the offer stage. Priority areas include 1) data, 2) ML / computational tooling, 3) scientific agents, and 4) harness & evals. Location: Ability to work from our San Francisco, CA; or Cambridge, MA office 3-5 days per week. Responsibilities: Deep Domain Ownership: We will match your unique product strengths to a specific scrum team, where you will go deep, define the roadmap, and become the absolute subject matter expert for that product area. That said, we operate as one product team and collaborate closely. Full-Stack Product Execution: From designing user-facing scientific workflow tools to shaping the underlying data inf [... source excerpt omitted ...] define requirements and guide execution across the entire stack. The intelligence platform for Science: Build intuitive, powerful software that scientists actually want to use. You will be responsible for translating incredibly complex scientific workflows into scalable, elegant digital products. Cross-Functional Orchestration: Lead the dialogue between our research scientists (who push the boundaries of what's possible) and our engineering teams (who build robust, scalable systems), ensuring we ship high-impact, cohesive products. Qualifications: Core Product Experience: Product Management experience building complex B2B SaaS, data-heavy platforms, or highly technical so [... source excerpt omitted ...] chnical realities of the backend architecture required to support it. Bias for Action in Complexity: You thrive in ambiguous environments. You can take a vague, highly technical customer request and turn it into a clear, prioritized roadmap for your engineering team in days, not weeks. Operational Rigor: You care deeply about the "how" — how the product is built, how it performs, and how it directly impacts the end user's daily work. Preferred Skills: Scientific Background: A degree or deep practical experience in the hard sciences (materials science, chemistry, biology, etc.). Building for Scientists: Direct experience building software, tools, or platforms specifically