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xAI

Infrastructure Finance Lead

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About xAI

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.

Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity.

We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important.

All engineers and researchers are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

About the Role

As the Infrastructure Finance Lead, you’ll shape xAI’s corporate finance strategy, driving operational KPI modeling to fuel our rapid growth. This role demands a relentless finance powerhouse—someone who can build cutting-edge financial models for a trailblazing AI company. Based in the Bay Area, you’ll be a critical member of our high-stakes, no-nonsense finance team, shaping the financial engine that powers our innovation. 

Responsibilities:

  • Partner with xAI’s technical team to provide actionable insights around all compute and storage investments and commercial deals
  • Build sophisticated models that help monitor and audit resource usage to track utilization and identify waste across teams, users and workloads.  
  • Regularly audit workloads to eliminate idle instances or over-provisioned resources
  • Negotiate cloud provider contracts that optimize for cost while meeting the needs of the business.  Provide recommendations on key financial terms around contract duration, commitment and reserved vs. on demand instances.
  • Track storage requirements for all aspects of the business and provide recommendations for balancing speed, availability and cost
  • Partner with the go to market team on strategic customer deals to understand margin profile under various performance outcomes.  Provide guidance and recommendations to ensure both xAI and customer are financially aligned
  • Understand financial implications of various hardware configurations for compute and inference clusters.  Build models that articulate tradeoffs in a clear way so business leaders can make informed decisions.
  • Stay up to date with best practices in infrastructure optimization.  Obsess over creative ways to optimize power usage, compute costs per workload, cooling efficiency and network bandwidth costs.
  • Deliver crisp, data-driven, actionable recommendations to leadership that drive immediate impact

Qualifications:

  • 5+ years of direct experience overseeing infrastructure costs of $1B+ per year
  • Preferred experience in modeling complex public cloud commercial deals
  • Preferred experience in modeling financials for owned compute clusters
  • Proven expertise in complex financial modeling is non-negotiable  
  • A high-octane performer who thrives in fast-paced, high-pressure environments—grit and resilience are essential.  
  • Exceptional natural analytical skills, paired with a proven track record of delivering under pressure.  
  • Elite communication abilities—capable of translating intricate financial data into clear, compelling narratives for diverse stakeholders.

Annual Salary Range

$160,000 - $200,000 USD

xAI is an equal opportunity employer and does not unlawfully discriminate based on race, color, religion, ethnicity, ancestry, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, disability, medical conditions, genetic information, marital status, military or veteran status, or any other applicable legally protected characteristics. 

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all applicable federal, state, and local laws, including the San Francisco Fair Chance Ordinance, Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 

For Los Angeles County (unincorporated) Candidates:

xAI reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: 

  • Access to information technology systems and confidential information, including proprietary and trade secret information, and/or user data;
  • Interacting with internal and/or external clients and colleagues; and
  • Exercising sound judgment.

California Consumer Privacy Act (CCPA) Notice

HQ

xAI San Francisco, California, USA Office

3180 18th St., San Francisco, CA, United States

xAI Palo Alto, California, USA Office

1450 Page Mill Road, Palo Alto, CA, United States

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

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