GPU Systems Engineer

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 Systems Engineer

Location: 100% Remote (U.S.)

Position Type: Full-time, Direct W2

Salary Range: $100,000–$150,000 Annually

Experience

Required:

6+ years

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

We are seeking a GPU Systems Engineer with deepexpertisein CUDA programming, GPU architecture, and high-performance computing to design andoptimizecompute-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, andhasa track recordof 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, andwill 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, anda track recordof shipping meaningful work that holds up well in production.

• 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 memoryutilizationfor 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 inPyTorch, JAX, or Triton.
• Collaborate with ML engineers toidentifyperformance bottlenecks in training and inference pipelines.
• Develop benchmarks and regression tests to safeguard performance over time.
• Evaluate new GPU architectures and feature sets, andadvise on adoption strategy.
• Contribute to compiler-level optimizations for tensor programs where appropriate, working at the boundary between ML frameworks and underlying acceleratorcodegento 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.

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, ora related field.
• Six or more years of experience in GPU programming and performance engineering.
• Deepexpertisein CUDA C/C++ and GPU programming models.
• Strong understanding of modern GPU architectures, memory hierarchies, and execution models.
• Hands-on experience profiling andoptimizingGPU workloads in production.
• Familiarity with NCCL, MPI, and high-performance interconnect technologies.
• Experience integrating…