GPU Software Engineer (CUDA)

Bright Vision Technologies•Des Plaines, IL

3d•$80,000 - $107,000•Remote

About The Position

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. We are seeking a GPU Software Engineer (CUDA) with deep expertise in CUDA programming, GPU architecture, and high-performance computing to design and optimize compute-intensive workloads on modern accelerator hardware. This role focuses on extracting maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads. The ideal candidate combines low-level systems mastery with strong software engineering practices, and has a track record of delivering measurable performance improvements on production GPU systems. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.

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.
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.

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.
Extract maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads.
Translate ambiguous requirements into well-engineered solutions.
Raise the bar through code review, design review, and mentorship of more junior engineers.