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Sail Research

Open-source AI inference for autonomous agents

Strategic Finance Lead

Corporate Finance & FP&AConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$200k - $300k /yr

About the Job

Requirements

  • Have 5–8+ years of experience in finance roles with substantial financial modeling experience, such as investment banking, growth equity or private equity, corporate development, or strategic finance or FP&A at a high-growth company.
  • Be able to build financial models from scratch, including an operating profit and loss statement and a pro forma model, without extensive guidance.
  • Be comfortable with ambiguity and able to apply judgment about what processes to build now versus later.

Responsibilities

  • Build and own Sail’s operating profit and loss model as the single source of truth connecting GPU cluster unit economics—including cost per GPU-hour, utilization, depreciation and useful life, and power costs—to product pricing, unit economics, and actual financial performance.
  • Partner cross-functionally on board materials, including financial modeling, data room preparation, and diligence support.
  • Establish Sail’s first financial planning and analysis process for budgeting and forecasting, appropriately sized for an early-stage company.
  • Develop the headcount and operating expense planning model as the team grows, and own burn-rate and runway tracking.

Desired Qualifications

  • Experience with infrastructure or capital-intensive businesses, such as data centers, energy, or equipment leasing.
  • Prior exposure to revenue recognition for usage-based or consumption billing, or experience in a GPU cloud, data center, or other infrastructure-as-a-service business.

About the Company

Sail Research

Open-source AI inference for autonomous agents

Sail Research provides AI inference services for open-source models at scale to power autonomous background agents. It offers a private-beta OpenAI-compatible API that supports open models like Deepseek, Kimi, and Qwen, plus fine-tuned versions, enabling token-serving for customers. It handles end-to-end work, including CUDA GPU optimization, tweaking inference engines, and distributing workloads across multiple providers with spot compute to reduce costs. Its goal is to help autonomous agents make meaningful independent progress on complex problems by delivering reliable, scalable, and affordable open-model inference through an OpenAI-compatible API.

Company size
11-50
Company stage
Series A
Total funding
$80M
Headquarters
San Francisco, CA
Founded
2026

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