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Lambda

Cloud-based GPU services for AI training

Senior Financial Planning and Analysis Analyst - Engineering & Product

Corporate Finance & FP&AConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Have a bachelor's degree in Finance, Accounting, Economics, or a related field.
  • Have 4 or more years of experience in FP&A, corporate finance, investment banking, or consulting, ideally focused on Cloud or AI infrastructure, SaaS, data centers, telecommunications, hardware, or industrials.
  • Have direct experience partnering with Engineering or Product organizations on budget, roadmap, and investment decisions.
  • Have extensive experience in financial modeling and analysis, including constructing complex financial models and interpreting financial statements to drive strategic decision-making.
  • Have strong Excel skills and experience with financial software systems.
  • Be able to work in a fast-paced, high-growth environment while balancing multiple complex projects.
  • Be able to work in an ambiguous environment with very little direction.

Responsibilities

  • Own project-level budgets, spend tracking, and cost-to-complete forecasts for Engineering and Product initiatives.
  • Quantify internal capacity consumption across development, testing, and benchmarking; build and maintain allocation and chargeback methodology.
  • Partner with Infrastructure and Engineering on fleet optimization, including utilization, idle capacity, instance mix, and reclamation opportunities.
  • Translate optimization opportunities into quantified savings and track realization against target.
  • Build business cases and return on investment models for product and research and development investments.
  • Establish post-investment measurement and track realized returns against the original underwriting.
  • Analyze research and development spend by initiative and support capitalization analysis with Accounting.
  • Provide prioritization frameworks and trade-off analysis for competing investment requests.
  • Serve as the finance partner across the new product introduction lifecycle, from concept through launch and ramp.
  • Model pricing, unit economics, margin, and volume assumptions for new offerings.
  • Own stage-gate financial deliverables and launch readiness criteria.
  • Track post-launch performance against the launch case and inform go/no-go and scaling decisions.
  • Own operating expense and headcount planning, tracking, and reporting for Engineering, Product, and Cloud Services organizations.
  • Maintain hiring plans, compensation assumptions, and organizational change tracking against approved headcount.
  • Prepare monthly and quarterly budget-versus-actual analysis, identify key drivers, and partner with Engineering and Product leaders to explain performance.
  • Manage vendor, software, and tooling spend; support accrual and reclassification inputs with Accounting through the monthly close.
  • Build and maintain the operating cash flow forecast model, including timing of vendor payments, capacity commitments, and project spend.
  • Own the rolling near-term cash view; reconcile forecast to actuals and explain variances.
  • Model scenarios around deployment timing, payment terms, and financing structures.
  • Partner with Accounting and Treasury on payables timing, prepaids, and commitment schedules.
  • Deliver Cloud Services, Engineering, and Product insights for monthly business reviews, executive reviews, and board materials.
  • Translate complex technical and operational data into clear financial insight.
  • Provide actionable recommendations on cost efficiency, investment prioritization, and margin improvement.

Desired Qualifications

  • Prior experience in a startup or high-growth organization, demonstrating adaptability and flexibility in those environments.
  • Direct experience building or owning a cash flow forecast.
  • Experience with research and development capitalization, new product introduction, or product profit and loss ownership.
  • Working proficiency in SQL and comfort querying large data sets directly.
  • Familiarity with graphics processing unit compute, artificial intelligence or machine learning workloads, or cloud infrastructure economics.

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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