ZipRecruiter

Log In

1

Contract Gpu Jobs (NOW HIRING)

Modal, Inc

Compute Strategy & Operations Lead

New York, NY · On-site

$200K - $300K/yr

... GPU and CPU capacity. You'll size our infrastructure needs ahead of demand, source supply across hyperscalers, neoclouds, and datacenter operators, and negotiate and close the contracts to secure it.

SF Compute

Account Executive

San Francisco, CA · On-site

$150K - $200K/yr

When people finance GPU clusters, the datacenters housing them, and the infrastructure powering them, they need "offtake" - meaning someone has signed a contract to lease the cluster for a period of ...

BitDeer

Sr. SRE Platform Architect

Austin, TX · On-site +1

$56.50 - $75/hr

This role will oversee the end-to-end architecture across CPU, GPU, RDS, storage, networking ... You author and evolve this contract. * Decide tier placement - what runs at Edge DC vs Regional ...

Staffingine LLC

Senior RHEL Systems Engineer

Boston, MA · On-site

$113K - $155K/yr

Contract * Installing, configuring, and managing enterprise software on RHEL platforms. * Providing technical support and troubleshooting for RHEL systems and GPU workstations. * Developing and using ...

next page

Showing results 1-20

All JobsContract Gpu Jobs

Contract Gpu information

See salary details

$41K

$106K

$139K

How much do contract gpu jobs pay per year?

As of Jul 14, 2026, the average yearly pay for contract gpu in the United States is $106,034.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,000.00 and $119,000.00 per year, depending on experience, location, and employer.

What is the difference between Contract Gpu vs Contract Data Scientist?

AspectContract GpuContract Data Scientist
Required CredentialsGPU certifications, technical skills in GPU programmingStatistics, programming, data analysis certifications
Work EnvironmentTech companies, research labs, AI firmsTech firms, finance, healthcare, consulting
Employer & Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights

Contract Gpu roles focus on GPU hardware and software expertise for AI and machine learning projects, while Contract Data Scientist roles emphasize data analysis, statistical modeling, and insights. Both are in tech-driven industries but serve different technical functions.

What are Contract GPU jobs?

Contract GPU jobs are temporary or project-based positions that require expertise in graphics processing units (GPUs). These jobs often involve developing, optimizing, or deploying GPU-accelerated applications, such as in machine learning, scientific computing, game development, or rendering. Contract workers may be hired by companies for specific tasks that need specialized knowledge in GPU programming using tools like CUDA or OpenCL. These roles can be remote or on-site and typically last for the duration of a particular project or set timeframe.

What are the key skills and qualifications needed to thrive as a Contract GPU Engineer, and why are they important?

To thrive as a Contract GPU Engineer, you need a strong background in computer science, GPU architecture, and parallel programming, often supported by a degree in a relevant field and prior experience with graphics hardware. Familiarity with tools and frameworks such as CUDA, OpenCL, DirectX, Vulkan, and performance profiling utilities is typically required. Excellent problem-solving, time management, and communication skills are crucial for collaborating with diverse teams and meeting project deadlines. These competencies ensure efficient development, optimization, and deployment of GPU-accelerated solutions in dynamic contract environments.

What are some common challenges faced by professionals working in contract GPU roles, and how can they address them?

Professionals in contract GPU roles often encounter challenges such as rapidly evolving hardware and software standards, tight project timelines, and the need to quickly adapt to different team environments. Staying updated with the latest GPU technologies and frameworks is essential, as is developing strong communication skills to collaborate effectively with full-time team members and stakeholders. To address these challenges, leveraging online resources, participating in relevant forums, and proactively seeking feedback can help contract GPU specialists deliver high-quality results and integrate smoothly into diverse project teams.
More about Contract Gpu jobs
What cities are hiring for Contract Gpu jobs? Cities with the most Contract Gpu job openings:
What are the most commonly searched types of Gpu jobs? The most popular types of Gpu jobs are:
What states have the most Contract Gpu jobs? States with the most job openings for Contract Gpu jobs include:
What job categories do people searching Contract Gpu jobs look for? The top searched job categories for Contract Gpu jobs are:
Contract Gpu jobs near you
Infographic showing various Contract Gpu job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 62% Full Time, 19% Part Time, 1% Temporary, and 17% Contract. Highlights an 80% Physical, 2% Hybrid, and 18% Remote job distribution, with an average salary of $106,034 per year, or $51 per hour.
CUDA Kernel Optimization Specialist

CUDA Kernel Optimization Specialist

Mercor

San Francisco, CA • Remote

$80 - $120/hr

Full-time

Re-posted 9 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: CUDA Engineering Expert
Type: Contract
Compensation: $80–$120/hour
Location: Remote

Role Responsibilities

  • Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
  • Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements.
  • Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
  • Write, modify, and reason about C++17, Python, and GPU programming code.
  • Apply CUDA, HIP, and shader programming expertise to improve performance outcomes.
  • Document optimization decisions clearly, noting when specific profiler metrics are useful.

Qualifications

Must-Have

  • Available to work at least 20 hrs/wk.
  • Fluent in core C++ features through C++17.
  • Working knowledge of Python and Git.
  • Fluent in at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL.
  • At least 1 year of professional or graduate-level research experience with GPUs.
  • Strong understanding of GPU profiler performance metrics for kernel optimization.
  • Ability to optimize GPU kernels without deep prior context on every algorithm.

Preferred

  • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization.
  • Experience optimizing kernels for NVIDIA Blackwell hardware.
  • Familiarity with NSight Compute.
  • Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm.
  • Open-source contributions related to GPU kernel optimization.

Application Process (Takes 20–30 mins to complete)

  • Submit your resume or relevant technical background to get started.
  • Qualified applicants may be asked to complete a brief technical assessment or submit additional information.

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.