Your Impact at LILA

We are seeking an Enterprise Account Lead, Chemicals to define how Fortune 500 energy, manufacturing, and chemical companies engage with the world's first platform for scientific superintelligence. You'll be a foundational member of the team responsible for building and scaling our enterprise sales organization and driving deal execution for Lila's AI-driven materials and chemistry platform.

As a revenue-focused individual contributor, you'll own and grow a portion of our physical sciences revenue pipeline, working across technical product, science, GTM, software, robotics, and Physical AI teams to translate our closed-loop AI and autonomous lab capabilities into signed enterprise contracts across catalyst, flow chemistry, and advanced materials.

This role blends technical depth with strong business acumen, an entrepreneurial mindset, and customer-centricity, and it is intentionally shaped to grow with the person who takes it on. You will prospect, qualify, and close enterprise deals, deliver structured market insights back to inform our product roadmap, and help to build out our GTM organization. In year one, you'll work across product and customer strategy, customer technical discovery, sales process design, and account leadership in one of the most technically demanding verticals in enterprise software.

What You'll Be Building

• Build, manage, and grow a high-quality pipeline against clear revenue targets, from opportunity identification and qualification through proposal, negotiation, and close.
• Lead complex, multi-stakeholder sales cycles across R&D, platform/technology, data/AI, and business leadership, coordinating internally to ensure tight execution and timely progress.
• Maintain accurate revenue forecasts and deal records, surfacing tradeoffs, priorities, and decisions for Lila's leadership team.
• Engage confidently with physical sciences buyers, speaking their language and helping customers understand where Lila fits in their R&D stack.
• Own a portfolio of strategic accounts end-to-end, building executive relationships and running account-level planning (whitespace, expansion, renewal).
• Drive day-to-day account execution: account research, meeting prep, customer-facing materials (pitch decks, proposals, briefs), structured follow-up, and stakeholder management.
• Partner with product, science, and business teams to scope high-value customer engagements, mapping customer needs and timelines against internal capabilities and resourcing.
• Coordinate internally across product, science, and leadership, bringing the right experts into customer conversations and unblocking technical inputs.
• Own technical RFI and DDQ response, routing scientific questions to internal experts and packaging polished customer-facing deliverables.
• Track the AI/ML partnership landscape across physical sciences and translate it into implications for Lila's positioning, messaging, and competitive strategy.
• Refine and maintain the revenue CRM, build BD tools and processes (proposal templates, delivery handoffs, governance), and define a repeatable GTM playbook (qualification criteria, battle cards, talk tracks).

What You'll Need to Succeed

• BS, MS, PhD, or equivalent experience in chemistry, physics, chemical engineering, or adjacent fields.
• 2-5+ years in a client-facing role where you owned outcomes for customers, across BD, enterprise sales, management consulting, customer success, or applied science (industrial, energy, or materials).
• Comfortable in a fast-moving, highly technical, cross-functional environment where the sales process, materials, and role itself are still being shaped.
• Strong written and verbal communication, able to translate technical depth into clear, customer-ready narratives for both R&D buyers and procurement teams.
• Highly organized and execution-oriented, comfortable managing multiple live accounts in parallel with limited oversight.
• Collaborative, low-ego working style, able to build trust quickly with PMs, scientists, engineers, and product leaders.
• Hands-on use of AI tools in day-to-day workflows and curiosity about how AI and autonomous science will reshape physical science.

Bonus Points For

• Working familiarity with AI/ML applied to physical sciences: property prediction, generative models for materials, Bayesian optimization, simulation-driven design.
• Direct exposure to enterprise R&D buying cycles in materials, semiconductors, or industrial biotech.
• Prior experience in frontier AI (SaaS, biotech, robotics, or data) selling into technical buyers.
• Prior experience as a founder or early GTM/commercial hire helping define early processes, playbooks, and collateral.
• A point of view on where AI is and is not useful in physical sciences R&D today.