▷ 15h Left: GPU Optimization Engineer
11 hours ago
Location: Remote
Job Type: Full-Time
About Us
We are an innovative company at the forefront of high-performance computing (HPC) and AI, building cutting-edge solutions powered by GPUs and specialized accelerators. We’re looking for a highly skilled GPU Optimization Engineer to design, develop, and optimize software running directly on bare-metal systems, leveraging the full potential of NVIDIA GPUs, AMD GPUs, and other accelerators.
Responsibilities
- Architect, develop, and optimize high-performance software for GPU-accelerated systems.- Design and implement software that directly interacts with GPU hardware, including NVIDIA CUDA, AMD ROCm, or OpenCL.- Optimize existing ML/DL frameworks (e.g., PyTorch, TensorFlow) for maximum performance on NVIDIA and AMD GPUs.- Work with heterogeneous systems, integrating specialized accelerators like AMD GPUs or custom chips (e.g., SambaNova, Cerebras).- Conduct profiling and tuning to maximize GPU utilization, minimize bottlenecks, and achieve peak system performance.- Collaborate with hardware engineers to exploit features like NVLink, NVSwitch, and RDMA for seamless GPU interconnectivity.- Develop scalable compute pipelines and contribute to performance benchmarking.
Requirements
Education:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
Experience:
- 5+ years in GPU-accelerated software development, HPC, or related domains.
Technical Skills:
GPU Programming
- Proficiency in CUDA for NVIDIA GPUs.- Experience with AMD ROCm, HIP, or OpenCL for AMD GPUs.
Frameworks
- In-depth knowledge of PyTorch or TensorFlow with GPU optimization.- Familiarity with ONNX and hardware-agnostic ML frameworks.
Languages
- Proficient in Python, C++, and/or C for performance-critical applications.
Profiling & Optimization
- Hands-on experience with GPU profilers such as NVIDIA Nsight, nvprof, and rocprof.- Ability to identify and mitigate bottlenecks in GPU pipelines.
System Knowledge
- Strong understanding of bare-metal systems, GPU drivers, and OS-level configuration.- Experience with containerized GPU environments (NVIDIA Docker, AMD ROCm containers).
Preferred Skills
- Experience with multiple accelerator platforms, such as SambaNova or Graphcore.- Familiarity with distributed computing and interconnects like NVLink, InfiniBand, or PCIe.- Knowledge of compiler optimization (LLVM, TVM, XLA).- Familiarity with Kubernetes for GPU cluster management.
What We Offer
- Competitive salary and benefits package.- Work with a talented and collaborative team of engineers.- Opportunities to work on cutting-edge GPU and HPC projects.- A flexible and dynamic startup environment where you can grow and innovate.
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