Director, FP&A, R&D

Location: Remote-US; Secondary Locations: Toronto (hybrid), San Francisco (hybrid); Workplace: RemoteFull-TimeDirector

Salary$176K - $250K; $176K – $250K • Offers Equity • Multiple Ranges; Toronto (Hybrid): Estimated base salary $176K – $210K • Offers Equity; US-National: Estimated base salary $194K – $229K • Offers Equity; San Francisco (Hybrid): Estimated base salary $212K – $250K • Offers Equity

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Job Details

Experience: 8+ years in FP&A / corporate finance, with meaningful time supporting R&D, Engineering, or a technical/product org.
Required Skills: SQLMicrosoft ExcelGoogle SheetsBigQuerydbt

Requirements

Have 8+ years of experience in FP&A or corporate finance.
Have meaningful experience supporting R&D, Engineering, or a technical/product organization.
Understand SaaS and AI/usage-based economics, including cost of revenue, gross margin, unit economics, and infrastructure and inference costs.
Have strong SQL skills; BigQuery experience is a major plus.
Have expert Excel or Google Sheets modeling skills and build clean, maintainable model architecture.
Understand how R&D spending flows through the P&L.
Be able to partner with technical leaders and translate technical work into financial views.
Nice to have: experience at a high-growth AI/ML company, familiarity with modern data stacks, contract negotiation, people management, or a CPA, CA, or CFA.
Nice to have: familiarity with dbt or EPMs and experience building repeatable, auditable finance pipelines.

Responsibilities

Partner with Engineering, Product, and Data leaders as their finance counterpart.
Build analyses for build-versus-buy, pricing, and infrastructure decisions.
Support foundation-model and cloud vendor negotiations with data-backed positions.
Deliver reporting and variance commentary to the CFO, FP&A leadership, and the board.
Own the R&D budget, forecast, and headcount plan across Engineering, Product, and Data/ML.
Drive re-forecasting and connect hiring plans to output and priorities.
Track LLM/inference and cloud compute costs, monitor spend, and work with Engineering on efficiency.
Model the relationship between usage, inference costs, and gross margin, including the effects of products, pricing, and infrastructure choices.
Evaluate major R&D spending decisions, including model and API contracts, cloud commitments, data, and development tools.

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