GPU Software Engineer (CUDA)

Bright Vision Technologies • Aurora, IL

1d • $80,000 - $107,000 • Remote

About The Position

Bright Vision Technologies is seeking a GPU Software Engineer with deep expertise in CUDA programming, GPU architecture, and high-performance computing. The role designs and optimizes compute-intensive workloads on modern accelerator hardware, with a focus on maximizing performance for AI training, inference, scientific computing, and high-throughput data processing. The engineer will work with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The position also involves raising engineering standards through code and design reviews and mentoring more junior engineers. The role is 100% remote in the United States and is a full-time, direct W-2 position. The listed salary range is $80,000–$107,000 annually. Bright Vision Technologies cannot sponsor new H-1B visa petitions for this position, but encourages applications from U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of experience in GPU programming and performance engineering; the listing also states 7+ years of experience required.
• Deep expertise in CUDA C/C++ and GPU programming models.
• Strong understanding of modern GPU architectures, memory hierarchies, and execution models.
• Hands-on experience profiling and optimizing GPU workloads in production.
• Familiarity with NCCL, MPI, and high-performance interconnect technologies.
• Experience integrating custom kernels into ML frameworks.
• Strong C++ skills and familiarity with modern systems programming practices.
• Solid grounding in linear algebra and numerical methods.
• Strong communication and collaboration skills with research and engineering teams.
• Clear communication and a track record of shipping meaningful work that performs reliably in production.

Nice To Haves

• Experience with Triton, CUTLASS, or other GPU kernel authoring frameworks.
• Familiarity with TensorRT, FasterTransformer, or vLLM internals.
• Exposure to compiler infrastructure such as LLVM or MLIR.
• Open-source contributions to GPU or ML performance libraries.
• Experience with large-scale distributed training infrastructure.

Responsibilities

• Design and optimize compute-intensive workloads on modern accelerator hardware.
• Improve GPU performance for AI training, inference, scientific computing, and high-throughput data processing workloads.
• Collaborate with product, design, engineering, operations, and business stakeholders to translate ambiguous requirements into well-engineered solutions.
• Participate in code reviews and design reviews.
• Mentor more junior engineers.
• Deliver measurable performance improvements on production GPU systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior