GPU Software Engineer (CUDA)
New
B
Bright Vision TechnologiesGPU computing
100% Remote (U.S.)Full-TimeSenior
Salary100,000 - 175,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- PyTorchC++
Requirements
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
- 10+ 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.
- Experience with Triton, CUTLASS, or other GPU kernel authoring frameworks is preferred.
- Familiarity with TensorRT, FasterTransformer, or vLLM internals is preferred.
- Exposure to LLVM or MLIR, open-source GPU or ML performance contributions, or large-scale distributed training infrastructure is preferred.
Responsibilities
- Design and implement high-performance CUDA kernels for AI and HPC workloads.
- Profile and optimize GPU code using Nsight Systems, Nsight Compute, and CUDA profilers.
- Tune memory access, occupancy, register usage, shared memory, and GPU memory hierarchy.
- Develop optimized libraries for linear algebra, attention, and other ML primitives.
- Optimize multi-GPU and multi-node training with 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.
- Develop benchmarks and regression tests to safeguard performance.
- Evaluate GPU architectures and contribute to compiler-level optimizations for tensor programs.
- Implement mixed-precision and quantized compute paths, and document performance characteristics and tuning decisions.
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