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Temporary Gpu Programming Jobs (NOW HIRING)

Nvidia

Hardware Validation Engineer

Santa Clara, CA · Hybrid

$145K - $191K/yr

More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a ... HSIOs like PCIE or chip-to-chip interconnects including understanding of process/temp/voltage ...

Nvidia

Senior IO Validation Engineer

Santa Clara, CA · Hybrid

$122K - $168K/yr

More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a ... HSIOs like PCIE or chip-to-chip interconnects including understanding of process/temp/voltage ...

TEKsystems

Openshift Platform Engineer

Chandler, AZ · Hybrid

$70 - $89/hr

Familiarity with GPU platforms, NVIDIA software stack, or high-performance AI infrastructure ... If eligible, the benefits available for this temporary role may include the following: • Medical ...

New

TEKsystems

Openshift Platform Engineer

Chandler, AZ · Hybrid

$70 - $89/hr

Familiarity with GPU platforms, NVIDIA software stack, or high-performance AI infrastructure ... If eligible, the benefits available for this temporary role may include the following: • Medical ...

New

TekWissen ®

Thermal Design Lead

Austin, TX · Hybrid

$60 - $67/hr

Temporary Assignment Pay Rate: $60.00-$67.00/hr Overview: TekWissen is a global workforce ... Collaborate with CPU/GPU architecture, performance, BIOS/SMU, packaging, and customer engineering ...

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How much do temporary gpu programming jobs pay per year?

As of Jun 19, 2026, the average yearly pay for temporary gpu programming in the United States is $64,974.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $80,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Temporary GPU Programmer, you generally need strong proficiency in parallel programming, C/C++, and deep knowledge of GPU architectures, often supported by a degree in computer science or a related field. Familiarity with technical tools like CUDA, OpenCL, and GPU profiling/debugging systems is typically required. Problem-solving ability, attention to detail, and effective communication are standout soft skills for this role. These skills ensure efficient development, optimization, and troubleshooting of GPU-accelerated applications in a fast-paced, project-based environment.

What types of projects or tasks are commonly assigned to temporary GPU programming roles?

Temporary GPU programming roles typically focus on short-term, high-impact projects such as optimizing existing code for parallel processing, accelerating specific algorithms, or supporting research and development teams with prototype implementations. You may be tasked with profiling and enhancing the performance of applications using frameworks like CUDA or OpenCL, or assisting with machine learning model training and inference on GPU hardware. Collaboration with data scientists, software engineers, and domain experts is common, and your contributions often involve delivering tangible speedups or enabling new computational capabilities within tight timelines.

What is a Temporary GPU Programmer?

A Temporary GPU Programmer is a professional hired on a short-term basis to develop, optimize, or maintain software that runs on Graphics Processing Units (GPUs). These programmers typically work with languages like CUDA, OpenCL, or DirectX to accelerate complex computations or graphics rendering. Temporary roles may be project-based, such as optimizing machine learning models, scientific simulations, or enhancing graphics in games and applications. Companies often hire temporary GPU programmers to meet specific deadlines, handle workload spikes, or bring in specialized expertise for certain tasks.
What are the most commonly searched types of Gpu Programming jobs? The most popular types of Gpu Programming jobs are:
Temporary Gpu Programming jobs near you
Lead Research Software Engineer, Portable AI Performance Engineering

Lead Research Software Engineer, Portable AI Performance Engineering

Massachusetts Institute of Technology

Cambridge, MA • On-site, Remote

$138K/yr

Other

Posted 3 days ago

Massachusetts Institute Of Technology rating

8.8

Company rating: 8.8 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

32nd of 538 rated colleges and universities

Job description

Lead Research Software Engineer, Portable AI Performance Engineering

  • Job Number: 25560

  • Functional Area: Information Technology

  • Department: MA Green High Performance Computing Ctr

  • School Area: MA Green High Performance Computing Ctr

  • Pay Range Minimum: $102,350

  • Pay Range Maximum: $138,700

  • Employment Type: Full-Time Temporary

  • Employment Category: Exempt

  • Visa Sponsorship Available: No

  • Schedule: 2-year position

  • Pay Grade: 10

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Posting Description

LEAD RESEARCH SOFTWARE ENGINEER, PORTABLE AI PERFORMANCE ENGINEERING, MA Green High Performance Computing Center, to be a hands-on research software engineering professional and serve as lead for applied performance engineering for AI workloads. Will work closely with research groups and leading computer industry collaborators to evaluate, adapt, and enhance the portable performance of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and will analyze and profile existing research AI workloads to identify performance bottlenecks and portability challenges; and port and optimize complex AI models and scientific code to run efficiently on AMD MI355X GPUs using ROCm, HIP, and related translation tools.

Job Requirements

REQUIRED: Bachelor’s degree or equivalent with a minimum of five years of work experience in either deeply technical fields and/or computational research experience; strong proficiency in Python and C++, with deep familiarity with AI/ML frameworks (PyTorch, TensorFlow, JAX); hands-on experience with GPU programming models (e.g., CUDA, HIP, or OpenCL); experience with performance profiling and benchmarking tools on Linux-based High-Performance Computing systems; excellent communication skills; ability to collaborate effectively with academic researchers and industry partners; and self-motivated with the ability to work independently in a remote or hybrid environment. PREFERRED: Direct experience with the AMD ROCm software stack and translating CUDA code to HIP; familiarity with AI agentic tools and Large Language Models (LLMs) used for code generation and refactoring; background in supporting large-scale, domain-specific scientific research (e.g., physics, biology, climate science) on institutional clusters; direct experience with one or more open-source schedulers and provisioners; experience with Linux container technologies such as LXC, apptainer and systemd-nspawn; or advanced degree in a relevant technical field.

The Lead Software Engineer must comply with all relevant MGHPCC security policies.

This is a two-year term position.

3/13/2026

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