GPU Software Engineer (CUDA)

Bright Vision Technologies•Monroe Township, MI

3d•$100,000 - $175,000•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 to design and optimize compute-intensive workloads on modern accelerator hardware. The role focuses on extracting performance from GPU platforms 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. They will contribute to code and design reviews and mentor more junior engineers, while applying strong engineering discipline and clear communication to deliver production-ready work. The position is full-time, direct W2, and 100% remote within the United States. The stated annual salary range is $100,000–$175,000. Applicants may include U.S. citizens, Green Card holders, EAD holders, and candidates transferring an H-1B; the employer cannot sponsor new H-1B petitions for this position.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
Ten 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 implement high-performance CUDA kernels for compute-intensive AI and HPC workloads.
Profile and optimize GPU code using Nsight Systems, Nsight Compute, and CUDA profilers.
Tune memory access patterns, occupancy, register usage, and shared memory utilization.
Develop 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 protect performance over time.
Evaluate new GPU architectures and feature sets and advise on adoption strategy.
Contribute to compiler-level optimizations for tensor programs at the boundary between ML frameworks and accelerator code generation.
Optimize use of HBM, L2, shared memory, and registers.
Implement mixed-precision and quantized compute paths that increase accelerator throughput while preserving numerical fidelity within acceptable bounds for target workloads.
Document performance characteristics, design decisions, and tuning playbooks for internal teams.
Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies.
Participate in code reviews and design reviews and mentor more junior engineers.
Work with cross-functional partners to translate ambiguous requirements into well-engineered solutions.