GPU Systems Engineer
About The Position
Bright Vision Technologies is seeking a GPU Systems Engineer 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 use low-level systems expertise and software engineering practices to deliver measurable performance improvements on production GPU systems. The engineer will collaborate with product, design, engineering, operations, and business stakeholders to translate requirements into solutions. The role also involves contributing to code and design reviews and mentoring more junior engineers. This is a full-time, direct W2 position, performed 100% remotely within the continental United States, on Bright Vision Technologies’ in-house SOW engagement. No third-party client, vendor, or implementation partner is involved. Salary is $100,000 to $150,000. No new H1B sponsorship is available, but qualified candidates needing an H1B transfer may apply. A technical coding assessment is mandatory.
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.
- Willingness to work directly as a full-time W2 employee of Bright Vision Technologies.
- Confidence in technical abilities and hands-on experience to complete a mandatory technical coding assessment.
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 safeguard 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 the GPU memory hierarchy, including HBM, L2, shared memory, and registers.
- Implement mixed-precision and quantized compute paths while preserving acceptable numerical fidelity.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
