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Lila Sciences is building Scientific Superintelligence to solve major scientific challenges through AI. As Finance Business Partner for the AI/ML and Infrastructure teams, you'll be the financial architect for a rapidly scaling organization, partnering with technology leaders to bring clarity to complex cost structures and drive resource allocation decisions.
You'll own the financial backbone for compute and GPU infrastructure, LLM usage and API spend, data storage, and ML pipelines. Your core responsibilities include: serving as primary finance partner to technical leaders across planning, performance management, and investment governance; translating AI and infrastructure roadmaps into clear P&L, cash, and capital implications; building dynamic planning processes and KPI frameworks that executives and the Board rely on; and developing financial models for headcount, OPEX, CAPEX, cloud spend, and LLM costs.
Specific deliverables include Board and investor materials, cash runway modeling, GPU and compute spend tracking with utilization reporting, LLM cost models and optimization frameworks, data storage lifecycle cost visibility, rolling forecasts, executive dashboards, and variance analyses. You'll also partner with Accounting, IT, and Procurement to improve data quality and cost allocation methodologies.
You're a builder-operator comfortable with imperfect datasets, able to reconcile to source systems, and skilled at translating technical complexity into clear narratives for executives. You thrive under tight timelines, balance precision with pragmatism, and influence cross-functionally.
Required: 7–10+ years in FP&A, strategic finance, investment banking, or consulting; startup/scale-up experience strongly preferred; hands-on zero-to-one build experience with driver-based models, planning, forecasts, and dashboards; demonstrated expertise in technology cost management (cloud, compute, GPU, or AI/ML); strong financial acumen and accounting fluency; Bachelor's in finance, economics, engineering, math, or related field.
Bonus: FinOps frameworks, LLM cost modeling, FP&A tool implementation, AI/ML infrastructure domain knowledge.
