GPU Systems Engineer

Role Description

We are seeking a GPU Systems 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.

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.

Requirements

• 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.

Benefits

• Competitive base salary commensurate with experience, plus benefits.
• 100% remote work opportunity.
• Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap.