ZipRecruiter

Log In

1

Software Engineer Gpu Jobs (NOW HIRING)

Futran Tech Solutions Pvt. Ltd.

GPU Software Engineer

$138.20K - $185.30K/yr

GPU Software Engineer Location: USA(Remote) Role Summary We are seeking expert-level GPU Software Engineers to support a high-visibility platform initiative within the Maya program, focused on ...

Nvidia

System Software Engineer - GPU

Santa Clara, CA

$203.20K - $240.80K/yr

We are seeking a System Software Engineer to work on next-generation computing and graphics products. Our charter is to build the most stressful set of applications a GPU or high performance ...

Baseten

Software Engineer - GPU Kernels

$143.30K/yr

Baseten is an innovative company powering AI solutions for leading firms like Notion and OpenEvidence, and they are seeking a GPU Kernel Engineer to enhance AI model performance. This role focuses on ...

Google

Senior Software Engineer, GPU Performance

Sunnyvale, CA · On-site

$143.80K - $189.50K/yr

Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.). About the job Google's software engineers develop the next-generation ...

Nvidia

System Software Engineer - GPU and SOC

Santa Clara, CA · On-site

$203.20K - $240.80K/yr

As a GPU/SOC system software engineer, you will work with a team of very dedicated software and hardware engineers involving a wide variety of technologies. As someone who is hardworking and ...

next page

Showing results 1-20

People also search for

All JobsSoftware Engineer Gpu Jobs

Software Engineer Gpu information

See salary details

$63.5K

$147.5K

$205.5K

How much do software engineer gpu jobs pay per year?

As of May 31, 2026, the average yearly pay for software engineer gpu in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Software Engineer GPU, you need strong programming skills in C/C++, parallel computing concepts, and a solid background in computer science or related fields. Familiarity with GPU programming frameworks such as CUDA or OpenCL, version control systems, and performance profiling tools is typically required. Analytical thinking, problem-solving abilities, and effective teamwork are essential soft skills for excelling in this role. These competencies ensure the development of optimized, high-performance software that leverages GPU architectures for demanding computational tasks.

How does a Software Engineer specializing in GPU typically collaborate with hardware and other engineering teams?

As a Software Engineer focusing on GPU, you will frequently work closely with hardware engineers, driver developers, and performance analysts. Collaboration often involves optimizing software to leverage GPU capabilities, troubleshooting performance bottlenecks, and ensuring compatibility with evolving hardware architectures. Effective communication and cross-functional teamwork are essential, as solutions often require aligning software design with hardware constraints and roadmaps. This collaborative environment not only broadens your technical understanding but also provides opportunities to learn from diverse engineering disciplines.

What are Software Engineer GPU roles?

A Software Engineer GPU is a specialist who designs, develops, and optimizes software that runs on Graphics Processing Units (GPUs). These engineers focus on maximizing the performance of applications—such as graphics rendering, machine learning, or scientific computation—by leveraging the parallel processing power of GPUs. They often work with languages like CUDA or OpenCL and collaborate with hardware teams to ensure efficient integration of software and GPU hardware. Their work is vital in industries like gaming, AI, automotive, and high-performance computing.

What is the difference between Software Engineer Gpu vs Software Engineer?

AspectSoftware Engineer GpuSoftware Engineer
Required SkillsGPU programming, parallel computing, CUDA/OpenCLGeneral software development, algorithms, coding
Work EnvironmentHigh-performance computing, graphics, AIWeb, mobile, enterprise applications
CertificationsCUDA certifications, relevant degreesVaries widely, often general CS degrees
Industry UsageGraphics, AI, scientific computingSoftware development across industries

Software Engineer Gpu specializes in GPU-based programming for high-performance tasks, while a Software Engineer has a broader focus on general software development. Both roles require strong coding skills, but GPU engineers focus more on parallel processing and graphics technologies. The choice depends on your interest in graphics and high-performance computing versus general software development.

What cities are hiring for Software Engineer Gpu jobs? Cities with the most Software Engineer Gpu job openings:
What are the most commonly searched types of Software Engineer Gpu jobs? The most popular types of Software Engineer Gpu jobs are:
What states have the most Software Engineer Gpu jobs? States with the most job openings for Software Engineer Gpu jobs include:
Software Engineer Gpu jobs near you
Infographic showing various Software Engineer Gpu job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 96% Physical, and 4% Hybrid job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

GPU Software Engineer

Futran Tech Solutions Pvt. Ltd.

Remote

$138.20K - $185.30K/yr

Full-time

Posted 17 days ago

Job description

Job Title : GPU Software Engineer
Location: USA(Remote)
Role Summary
We are seeking expert-level GPU Software Engineers to support a high-visibility platform initiative within the Maya program, focused on building software tooling on top of a custom compiler and SDK.
The role involves developing, optimizing, and porting GPU kernels and AI workloads to a specialized hardware platform.
This is a critical and time-sensitive engagement with immediate onboarding expectations and long-term roadmap alignment (~18 months).
Key Responsibilities
• Develop GPU kernels for specialized hardware platforms using PyTorch/Triton frameworks
• Build software solutions leveraging custom compiler and SDK capabilities
• Design and implement kernel-level optimizations to control hardware execution behavior
• Port open-source AI/ML models to custom SDK environments
• Port and adapt high-performance computing benchmarks and stress workloads such as:
  • Linpack (High Performance Linpack)
  • BERT/benchmark-style workloads (referred as "Babu bench")
    • Develop stress testing and validation workloads aligned to hardware behaviour and platform validation
    • Support testing and stress testing of current and next-generation hardware platforms
    • Collaborate closely with platform architects and compiler teams to enhance system capabilities

Core Technical Skills (Must-Have)
Programming & Frameworks
• Python
• C/C++ (systems-level programming)
• PyTorch
• Triton (Triton language / kernel development)
GPU & Systems Expertise
• GPU kernel development (mandatory and critical)
• Strong understanding of GPU architecture and compute optimization
• Experience with compiler-based optimizations / runtime execution layers
• Experience with custom SDKs or hardware abstraction layers
Performance & Workloads
• Experience in:
  • GEMM kernel development (matrix multiplication kernels)
  • Porting ML models to new hardware platforms
  • Performance tuning and stress testing at system level

Nice-to-Have Skills
• Experience working with custom silicon / hardware platforms
• Exposure to high-performance computing (HPC) workloads
• Familiarity with:
  • Linpack benchmarks
  • AI workload benchmarking tools
    • Experience in compiler optimization ecosystems

Engagement Model & Structure
• Number of roles: 3 developers (initial hiring may start with 2)
• Location flexibility:
  • Onsite / Offshore / Hybrid mix allowed
    • Timeline:
  • Immediate start required
    • Duration:
  • ~18 months program duration with phased platform evolution

Interview Process
• Candidates will undergo direct technical evaluation by program lead
• Strong preference for candidates who can showcase real implementations / past work (hands-on kernel development)
Key Differentiators (Critical Expectation)
• This is NOT a DevOps / support / debugging role
• Requires deep hands-on engineering expertise in:
  • Kernel programming
  • GPU workloads
  • ML framework internals
    • Candidates must demonstrate build-level competence, not just theoretical knowledge

Success Criteria
• Ability to deliver:
  • High-performance kernels
  • Production-ready software for hardware platforms
    • Successful porting of models and workloads to custom environments
    • Contribution to next-generation platform readiness and validation

✅ Recommended Screening Criteria
To help you send the right candidates quickly, prioritize profiles with:
• Proven GPU kernel development experience (non-negotiable)
• Hands-on PyTorch + Triton kernel implementation
• Evidence of systems-level programming (C/C++)
• Contributions to AI infrastructure, HPC, or compiler-level work

Apply