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Lila is building a platform for scientific superintelligence, applying closed-loop AI and autonomous lab capabilities to materials science and chemistry. As Enterprise Account Lead for Chemicals, you'll be a foundational sales hire responsible for defining how Fortune 500 energy, manufacturing, and chemical companies adopt Lila's AI-driven platform.
You'll own and grow a portion of the enterprise revenue pipeline, working across product, science, GTM, software, and robotics teams to translate autonomous lab capabilities into signed contracts in catalyst, flow chemistry, and advanced materials. This is a revenue-focused individual contributor role that blends technical depth with business acumen and customer-centricity.
Key responsibilities include: building and managing a high-quality pipeline against clear revenue targets; leading complex, multi-stakeholder sales cycles across R&D, platform, and business leadership; maintaining accurate forecasts and deal records; engaging confidently with physical sciences buyers; owning a portfolio of strategic accounts end-to-end; driving day-to-day account execution including research, proposals, and follow-up; partnering with product and science teams to scope customer engagements; coordinating internal experts for customer conversations; owning technical RFI and DDQ responses; tracking the AI/ML partnership landscape in physical sciences; and refining CRM processes, building BD tools, and defining repeatable GTM playbooks.
You'll need a BS, MS, PhD, or equivalent in chemistry, physics, chemical engineering, or adjacent fields, plus 2-5+ years in a client-facing role (BD, enterprise sales, consulting, customer success, or applied science) where you owned customer outcomes. You should be comfortable in fast-moving, highly technical, cross-functional environments; excel at translating technical depth into clear narratives for both R&D and procurement buyers; be highly organized and execution-oriented; work collaboratively with low ego; and use AI tools in daily workflows.
Bonus experience includes working familiarity with AI/ML applied to physical sciences (property prediction, generative models, Bayesian optimization, simulation-driven design); direct exposure to enterprise R&D buying cycles in materials, semiconductors, or industrial biotech; prior frontier AI sales experience; early-stage GTM/commercial hiring experience; and a point of view on AI's utility in physical sciences R&D.
