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GPU Systems Engineer, CUDA
Bright Vision TechnologiesGPU Systems Engineer developing high-performance CUDA applications for innovative solutions at Bright Vision Technologies. Collaborating with cross-functional teams to optimize GPU workloads across AI and HPC.
Tech Stack
Tools & technologiesNode.jsPyTorch
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Competitive base salary commensurate with experience, plus benefits
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
CUDAC/C++GPU programmingperformance engineeringlinear algebramixed-precision computequantized computememory optimizationcustom kernelsML primitives
Soft Skills
communicationcollaboration