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Lambda

Cloud-based GPU services for AI training

Senior Financial Planning and Analysis Analyst - Supply Chain Finance

Corporate Finance & FP&AConfirmed live in the last 24 hours
Full-time$152k - $225k /yr

About the Job

Requirements

  • Have a bachelor's degree in Finance, Accounting, Economics, Supply Chain, or a related field.
  • Have 4+ years of experience in FP&A, supply chain finance, procurement finance, hardware finance, or a related analytical role.
  • Have experience forecasting equipment or inventory spending, analyzing supplier costs, or managing purchase commitment and working capital reporting.
  • Understand how purchase orders, deposits, receipts, supplier payment terms, and inventory movements affect capital spending, in-service timing, depreciation, and cash forecasts.
  • Possess strong financial modeling and Excel skills, including the ability to analyze unit costs, supplier alternatives, and purchasing scenarios.
  • Have working proficiency in SQL and experience working with ERP and procurement data, such as NetSuite and Coupa.
  • Be able to reconcile detailed procurement and financial data, investigate variances, and turn findings into practical recommendations.
  • Communicate clearly and partner effectively with sourcing, purchasing, planning, operations, and accounting teams.
  • Be comfortable working independently in an ambiguous, fast-paced environment while managing multiple projects and deadlines.

Responsibilities

  • Build and maintain equipment capital spending forecasts across GPUs, servers, networking, storage, and spares by supplier, platform, and deployment.
  • Translate procurement quantities, unit costs, delivery schedules, and acceptance milestones into equipment spending and deployment cost forecasts.
  • Track actual costs and purchase commitments against approved investment budgets, and explain changes in price, quantity, configuration, and delivery timing.
  • Own equipment in-service timing from delivery and acceptance through deployment, and forecast resulting depreciation by asset class, platform, and site.
  • Combine equipment in-service schedules with facility in-service dates from DC Finance to produce the consolidated infrastructure depreciation forecast.
  • Reconcile forecast depreciation to the fixed asset register and explain changes driven by timing, useful life, and cost.
  • Analyze supplier quotes, bills of materials, and total landed costs, including freight, storage, duties, warranties, and other applicable charges.
  • Model the financial impact of supplier alternatives, volume commitments, payment terms, and component cost changes to support sourcing negotiations and recommendations.
  • Track negotiated savings and cost increases against approved assumptions, distinguishing expected savings from realized financial impact.
  • Own the team's commitments data across equipment and construction spend, connecting purchase orders, receipts, invoices, and payments into a single source used by DC Finance and Corporate FP&A.
  • Maintain financial visibility into open purchase orders, deposits, prepayments, contractual commitments, and remaining equipment spending.
  • Own supplier payment forecasts using contractual terms, delivery and acceptance dates, and invoice status as inputs to the consolidated cash forecast for Corporate FP&A and Treasury.
  • Assess the cost and cash implications of order changes, delivery delays, expedited shipments, and cancellation or rescheduling provisions.
  • Partner with Supply Chain to track financial exposure associated with equipment on order, in transit, in storage, and awaiting deployment.
  • Evaluate aging inventory, spares levels, excess equipment, and obsolescence risk; quantify the cost of holding inventory and potential mitigation options.
  • Work with capacity planning, DC Finance, and Business / Commercial Finance to align purchasing assumptions with site readiness and demand forecasts, highlighting mismatches and cash tied up in idle equipment.
  • Develop equipment, logistics, and supply-related working capital inputs for the annual operating plan, quarterly forecasts, and long-range infrastructure plan.
  • Partner with Accounting and Supply Chain to reconcile purchase orders, receipts, invoices, deposits, and accrual inputs, and support consistent project and asset cost attribution.
  • Coordinate equipment cost assumptions with DC Finance and provide financial analysis to Treasury and Capital Markets for equipment funding evaluations.
  • Prepare supplier, equipment cost, commitment, and inventory reporting for business reviews, executive discussions, and investment recommendations.
  • Provide equipment cost inputs and analysis for investment committee reviews, and compare realized equipment costs and delivery timing with approved assumptions after deployment.
  • Build and maintain repeatable models and workflows in Pigment and AI-assisted tools, reducing manual spreadsheet work.
  • Improve procurement data quality and connect purchasing, inventory, and financial systems to produce repeatable forecasts and clear recommendations.

Desired Qualifications

  • Have experience in hardware, OEM/ODM, or semiconductor supply chain finance, ideally with GPU, server, or networking equipment.
  • Have familiarity with inventory accounting, fixed asset accounting, and depreciation.
  • Have experience with vendor financing, equipment leases, or other equipment funding structures.
  • Have experience with Pigment or another enterprise planning system, and business intelligence tools such as Tableau.
  • Have familiarity with data center deployment ramps, site readiness, or cloud infrastructure unit economics.
  • Have prior experience in a startup or high-growth organization.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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