Director, Strategic Finance - Corporate FP&A

Responsibilities

• Own company-wide corporate FP&A, including the company model, long-range and annual planning, monthly forecasting, and revenue forecasting.
• Lead G&A budgeting, headcount planning, and spend management as the primary finance partner to G&A leadership.
• Prepare and deliver financial reporting, insights, and commentary for executive leadership, the Board, and investors.
• Build and maintain a decision-quality KPI and reporting architecture, including scorecards and dashboards.
• Develop automated financial infrastructure and tooling to scale output for a high-growth, newly public company.
• Support public-company reporting rhythms, guidance support, and investor-grade information delivery.

Requirements

• 15+ years of total professional experience, ideally in corporate FP&A, strategic finance, or adjacent roles.
• Proven leadership experience in Corporate FP&A, preferably within a public or newly public company environment.
• Exceptional financial modeling skills with the ability to build complex models from scratch and defend assumptions.
• Extensive experience in executive-facing partnership, earning trust through fast and accurate insights.
• High agency and systems thinking, with the ability to operate effectively in ambiguous, high-growth settings.
• Technical fluency in Excel and familiarity with SQL, Python, or other data/BI tools.

Preferred Qualifications

• Experience in hardware, semiconductor, or complex infrastructure industries.
• Background in navigating rapid scale, guidance support, and public-company planning rhythms.
• Proficiency with ERP and BI systems such as NetSuite, Oracle, or Looker.
• Strong affinity for AI tools to automate workflows and accelerate financial analysis.

About the Company

Cerebras Systems builds the world's largest AI chip, providing the compute power of dozens of GPUs on a single wafer-scale architecture. Our technology delivers industry-leading training and inference speeds, empowering the world's leading AI model labs and enterprises to run large-scale machine learning applications with unprecedented efficiency.