CUDA Developer at Bright Vision Technologies — Remote

CUDA Developer – Remote

Job Title: CUDA Developer Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $85,000–$110,000 Annually Experience Required: 6+ years

Job Summary We are seeking a CUDA Developer 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

How to Apply