5-7 years of experience in systems engineering or related field
Proficiency in advanced C++ (C++17/20 preferred)
Experience with parallel programming frameworks (CUDA, HIP, etc.)
Strong understanding of high-performance computing (HPC) principles
Familiarity with performance tooling and systems optimization techniques
Responsibilities
Design and optimize GPU kernels and tensor libraries
Translate HPC techniques to scalable AI inference solutions
Evaluate emerging architectures and resource management approaches
Collaborate with technical leadership to enhance GPU infrastructure efficiency
Benefits
Comprehensive health, dental, vision, and life insurance
401(k) plan with company match
Meaningful early-stage equity participation
Onsite meals and snacks provided
Opportunities for close collaboration with founders and tech leaders
Dynamic, fast-paced startup culture that rewards initiative
Full Job Description
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. 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
