GPU Engineer
Job in
San Jose, Santa Clara County, California, 95199, USA
Listed on 2026-05-10
Listing for:
Ultimate Staffing Services
Full Time
position Listed on 2026-05-10
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer, C++ Developer, Computer Science
Job Description & How to Apply Below
Salary: USD
87360 - USD
104000 per year
A GPU Engineer designs, develops, and optimizes software and systems that run on Graphics Processing Units (GPUs). The role focuses on high‑performance computing, graphics rendering, or compute workloads such as AI/ML, computer vision, and scientific simulations, ensuring maximum performance, scalability, and efficiency.
Key Responsibilities- Design, develop, and optimize GPU‑accelerated software solutions
- Write and maintain high‑performance code using CUDA, OpenCL, Vulkan, Direct
X, or Metal - Optimize memory usage, latency, and throughput on GPU architectures
- Develop and debug graphics pipelines
, shaders, or compute kernels - Profile GPU performance and resolve bottlenecks using diagnostic tools
- Collaborate with CPU, driver, and hardware teams to ensure efficient integration
- Support AI/ML workloads by accelerating neural networks and data pipelines
- Analyze and adapt algorithms for parallel processing
- Ensure code scalability across different GPU architectures
- Document designs, performance benchmarks, and optimization strategies
- Bachelor’s in Computer Engineering, Electrical Engineering, Computer Science
, or related field - Strong programming skills in C/C++ and at least one GPU programming framework (CUDA, OpenCL, etc.)
- Deep understanding of parallel computing concepts
- Experience with GPU memory models
, threads, and synchronization - Familiarity with graphics APIs (OpenGL, Vulkan, Direct
X) or compute APIs - Experience debugging and profiling GPU applications
- Experience with AI/ML frameworks (Tensor Flow, PyTorch, ONNX)
- Knowledge of computer graphics
, ray tracing, or real‑time rendering - Familiarity with driver-level or kernel-level GPU development
- Experience optimizing for embedded or mobile GPUs
- Understanding of hardware architecture (SMs, warps, caches, pipelines)
- NVIDIA CUDA / Nsight
- AMD ROCm
- Python for experimentation and tooling
- Performance profiling and benchmarking tools
- Strong problem-solving and analytical skills
- Ability to explain complex performance concepts clearly
- Collaboration across hardware, firmware, and software teams
- Attention to detail and performance trade‑offs
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