GPU Software Engineer (CUDA)
About The Position
Bright Vision Technologies is seeking a GPU Software Engineer (CUDA) to design and optimize compute-intensive workloads on modern accelerator hardware. The role focuses on maximizing GPU performance for AI training, inference, scientific computing, and high-throughput data processing. The successful candidate will bring deep CUDA programming, GPU architecture, and high-performance computing expertise, along with strong software engineering practices and a record of delivering measurable performance improvements on production GPU systems. The engineer will work with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The role also includes contributing to code and design reviews and mentoring more junior engineers. This is a full-time, direct W2 position that is 100% remote within the United States.
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.
- Improve GPU performance for AI training, inference, scientific computing, and high-throughput data processing workloads.
- Profile and optimize GPU workloads in production.
- Integrate custom kernels into ML frameworks.
- Collaborate with product, design, engineering, operations, and business stakeholders to translate requirements into solutions.
- Contribute to code reviews and design reviews.
- Mentor more junior engineers.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
