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Lila Sciences

Finance Business Partner, AI & Software

Cambridge, MA USAmidAdded today

About this role

Finance Business Partner role supporting AI/ML and Infrastructure teams at a scaling AI-driven scientific research company. You'll build financial planning frameworks, cost models, and reporting systems to drive visibility and decision-making across compute, GPU, LLM, and data infrastructure spending.

What you'll do

  • Serve as primary finance partner to AI/ML and Infrastructure teams, owning planning, performance management, and investment governance
  • Build and maintain financial models for compute, GPU, LLM API costs, and data infrastructure with rolling forecasts
  • Develop KPI dashboards, reporting packages, and variance analyses for executives and budget owners
  • Partner with Technology leaders to translate infrastructure roadmaps into P&L, cash, and capital implications
  • Lead headcount and capacity planning for technical teams with tracking of hiring pace and resource allocation
  • Support fundraising efforts with Board materials, investor analyses, and diligence preparation

What they're looking for

  • FP&A and financial planning
  • Cloud and compute infrastructure cost modeling
  • LLM and AI/ML cost structures
  • Driver-based financial modeling and forecasting
  • FinOps and cost optimization frameworks
  • Financial analysis and reporting
  • Stakeholder communication and influence
  • Data integration and reconciliation
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Lila Sciences

Lila Sciences builds AI and automation systems for scientific research, including tools for automated analysis, control systems for lab operations, and large language models for scientific tasks. The company is hiring software engineers, machine learning scientists, research engineers, and automation specialists to develop and maintain these scientific computing platforms.

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Likely interview questions

  • Tell us about a time you built a financial planning framework from scratch—what was the most complex cost structure you had to model and how did you approach it?
  • Describe your experience managing cloud or GPU infrastructure costs—what optimization opportunities have you identified and quantified?
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