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Lila is building a platform for scientific superintelligence applied to materials and chemistry. As Enterprise Account Lead, Materials, you'll be a founding member of the enterprise sales organization, owning and growing a revenue pipeline focused on Fortune 500 energy, manufacturing, and materials companies.
You'll prospect, qualify, and close enterprise deals while translating Lila's closed-loop AI and autonomous lab capabilities into signed contracts across catalyst, flow chemistry, and advanced materials. This role blends technical depth with business acumen and entrepreneurial mindset. You'll manage complex, multi-stakeholder sales cycles involving R&D, platform/technology, data/AI, and business leadership teams.
Key responsibilities include: building and managing a high-quality pipeline against revenue targets; leading complex sales cycles with internal coordination; 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 technical RFI and DDQ responses; tracking the AI/ML partnership landscape; and refining CRM processes and GTM playbooks.
You'll need a BS, MS, PhD, or equivalent in materials, physics, chemical engineering, or adjacent fields, plus 2-5+ years in a client-facing role (BD, enterprise sales, consulting, customer success, or applied science). You should be comfortable in fast-moving, highly technical, cross-functional environments; excel at translating technical depth into clear narratives; be highly organized and execution-oriented; work collaboratively with low ego; and use AI tools in daily workflows.
Bonus qualifications include familiarity with AI/ML applied to physical sciences, direct exposure to enterprise R&D buying cycles in materials/semiconductors/biotech, prior frontier AI sales experience, early-stage GTM/founder experience, and a point of view on AI's utility in physical sciences R&D.
