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Vast.ai

GPU Systems Engineer – HPC / Parallel Computing

Sorry, this job was removed at 09:46 a.m. (PST) on Thursday, Sep 17, 2026
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In-Office
San Francisco, CA, USA
160K-320K Annually
Mid level
In-Office
San Francisco, CA, USA
160K-320K Annually
Mid level

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Seeking a systems engineer with HPC or parallel programming experience to optimize GPU performance for AI inference at Vast.ai.
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About Us

Vast.ai’s cloud powers AI projects and businesses all over the world. We are democratizing and decentralizing AI computing—reshaping our future for the benefit of humanity.

We are a small, growing, and highly motivated team dedicated to an ambitious technical plan. We operate with a flat mobile organizational structure where all contribute directly to the company’s mission. Leadership is earned by those who show initiative and deliver excellence. 

We seek engineers/researchers with strong intrinsic drive, a true passion for advancing the state of the art, and a mix of excellent research, coding, and communication skills.

LOCATION: On-site at our office in San Francisco or Westwood, Los Angeles.

About the Role

We’re looking for a systems engineer with HPC or parallel programming experience to help scale AI inference. You’ll leverage your knowledge of high-performance systems to optimize GPU performance at the bleeding edge of AI.

  • Full-Time
  • On-site at either our SF or LA offices
Tech Stack

CUDA/C++, GPGPU, Python, Linux

Key Responsibilities
  • Design and optimize GPU kernels and tensor libraries
  • Translate HPC techniques into scalable AI inference solutions
  • Evaluate emerging architectures and resource management approaches
  • Collaborate with technical leadership to improve GPU infrastructure efficiency
Ideal Experience
  • Advanced C++ (C++17/20 preferred)
  • Expertise with at least one parallel framework (CUDA, HIP, SYCL, OpenCL, OpenACC, or similar)
  • Strong background in systems optimization and HPC performance tooling
  • Familiarity with distributed training/inference frameworks (bonus)
Interview Process

After submitting your application, our technical team reviews your credentials. If selected, you'll proceed through the following stages:

  • Initial screening (virtual, 15 minutes)
  • Quick dive into Vast, systems and architectures (virtual, 30 minutes)
  • LLM-assisted coding assessment (virtual, 1 hour)
  • Meet and greet with coding assessment (on-site, 2 hours)
Our goal is to complete the interview process in two weeks.Annual Salary Range

$160,000 – $320,000 + equity + benefits

Vast.ai is hiring across all experience levels with compensation commensurate with background, experience and potential.

Benefits
  • Comprehensive health, dental, vision, and life insurance
  • 401(k) with company match 
  • Meaningful early-stage equity
  • Onsite meals, snacks, and close collaboration with founders/tech leaders
  • Ambitious, fast-paced startup culture where initiative is rewarded

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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