Finance Manager – Infrastructure, Capacity

Runpod

. Maintain a live view of available capacity across GPU types and datacenters .

Posted 10/12/2026full-timeRemote • United StatesMid-LevelSenior💰 $140,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in financial modeling, forecasting, and capacity planning, with a strong focus on GPU and non-GPU infrastructure investments. Proficient in data analysis and negotiation, translating complex datasets into actionable business insights.

Highest-signal resume keywords

Financial ModelingForecastingExcel/Google Sheets ProficiencySQL QueryingCapacity Planning

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Financial ModelingForecastingCapex AnalysisCash Flow AnalysisReturn AnalysisLease-Versus-Buy AnalysisRevenue-Share StructuresFleet PlanningOperational Dataset AnalysisScenario Analysis

Soft Skills

Negotiation SkillsCommunication SkillsProblem-SolvingInitiative

Tools & Technologies

ExcelGoogle SheetsBI Tools

Industry Keywords

AI InfrastructureCloudData CenterUS GAAPLease AccountingFixed AssetsHigh-Growth StartupVenture-Backed Technology

Tech Stack

Tools & technologies

CloudNode.jsSQL

About the role

Key responsibilities & impact

Maintain a live view of available capacity across GPU types and datacenters
Serve as the company's source of truth for available, sold, and upcoming capacity
Drive fill on upcoming capacity tranches with Sales and Infrastructure
Monitor fleet utilization and address underperforming SKUs, datacenters, and node groups
Evaluate GPU ownership models including revenue share, leasing, and direct purchase
Forecast cash requirements and own the capacity capex plan
Model revenue impact of bring-up timing and quantify delay costs
Evaluate and monitor non-GPU infrastructure investments, including storage and CPU capacity
Build fleet unit economics, including contribution margin by GPU type, datacenter, and host
Set pricing floors and return hurdles for capacity decisions
Support commercial negotiations with hosts and datacenter partners
Model term sheets, revenue-share splits, and lease economics
Run scenario and risk analysis on the supply book
Own capacity reporting dashboards and post-commitment variance analysis
Report to Runpod's Director of Finance and partner with Infrastructure, Data Center Operations, and Sales

Requirements

What you’ll need

4+ years of experience in finance, FP&A, corporate development, or a related analytical role
Strong foundation in financial modeling and forecasting
Advanced proficiency in Excel/Google Sheets modeling, including capex, cash flow, and return analysis (NPV, IRR, payback)
Comfort working with large operational datasets and BI tools
Comfort driving ambiguous initiatives to completion with minimal supervision
Strong negotiation and communication skills, with the ability to translate data into clear recommendations for business stakeholders
Successful completion of a background check
Experience in a high-growth startup or venture-backed technology company preferred
Experience underwriting capital-intensive investments preferred
Experience with lease-versus-buy analysis, equipment financing, or revenue-share structures preferred
Experience in capacity, supply, or fleet planning preferred
SQL or hands-on experience querying large operational datasets preferred
Working knowledge of US GAAP, including lease accounting (ASC 842) and fixed assets preferred
Experience in the AI infrastructure, cloud, or data center industry preferred
Must be eligible to work in the United States; Runpod is currently unable to sponsor employment visas

Benefits

Comp & perks

Meaningful equity in a fast-growing company; everyone on the team receives stock options
Generous medical, dental & vision plans
Flexible PTO
Remote-first work environment
$1,200 Home Office & Equipment Stipend
Passionate team working on AI infrastructure with learning and ownership opportunities