GPU Software Engineer (CUDA)

GPU Software Engineer (CUDA)– Remote

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.

Job Title: GPU Software Engineer (CUDA)
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$175,000 Annually
Experience Required: 6+ years

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary:
We are seeking a GPU Software Engineer 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.

Key Responsibilities
- Design and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use cases.

- Profile and optimize GPU code using tools such as Nsight Systems, Nsight Compute, and CUDA profilers.

- Tune memory access patterns, occupancy, register usage, and shared memory utilization for peak performance.

- Develop highly optimized libraries for linear algebra, attention, and other ML primitives.

- Optimize multi-GPU and multi-node training using NCCL, RDMA, and high-performance networking.

- Implement custom operators and fused kernels in PyTorch, JAX, or Triton.

- Collaborate with ML engineers to identify performance bottlenecks in training and inference pipelines.

- Develop benchmarks and regression tests to safeguard performance over time.

- Evaluate new GPU architectures and feature sets, and advise on adoption strategy.

- Contribute to compiler-level optimizations for tensor programs where appropriate, working at the boundary between ML frameworks and underlying accelerator codegen to unlock performance not reachable through framework-level tuning alone.

- Optimize memory hierarchy usage across HBM, L2, shared memory, and registers.

- Implement mixed-precision and quantized compute paths that maximize accelerator throughput while preserving numerical fidelity within bounds acceptable for the target workloads.

- Document performance characteristics, design decisions, and tuning playbooks for internal teams.

- Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies.

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