Strategic Finance Lead - Infrastructure
Perplexity · San Francisco, CA · 2 wk ago
HybridFinance$190k–$230k/yrFull-time
Responsibilities
- Build and maintain multi-year capacity forecasts and scenario models across CPU, GPU, and accelerator footprints
- Partner with Strategic Finance leadership on long-horizon capacity decisions, including reserved/committed structures and build-out sequencing
- Lead ROI and trade-off analysis for new platforms, hardware generations, and efficiency initiatives
- Translate engineering and product roadmaps into capacity requirements and the cost envelope to support them
- Own day-to-day management for all core infrastructure spend, including the monthly bill, variance analysis, and forecasting
- Drive efficiency and optimization initiatives with engineering and infrastructure teams
- Build cost-driver models that explain spend movements and surface the levers behind them
- Support CSP rate and commit negotiations with rate analysis, benchmarking, and contract modeling
- Maintain deep working knowledge of CSP contract terms, pricing structures, discount programs, and optimization opportunities
- Partner with Accounting on contract operationalization, accrual accuracy, and compliance
Qualifications
- 4+ years of experience in strategic finance or FP&A at a high-growth technology company, plus 2+ years in investment banking at a top-tier firm
- Strong financial modeling skills and the ability to translate complex operating data into clear, decision-ready analysis
- A track record of partnering effectively with engineering or technical teams to drive financial outcomes
- Comfort communicating complex financial concepts to non-finance audiences across the company
- Bias for action, first-principles thinking, and an ability to make progress in ambiguous environments
- Excitement about working in a fast-paced, hyper-growth setting and adapting quickly as priorities shift
- Strong process discipline, business judgment, and cross-functional communication skills
- Genuine interest in AI infrastructure and the cost dynamics of large-scale model serving and training