GPU Optimization Engineer
3 days ago
Job Posting: GPU Optimization Engineer (Bare Metal Expertise)
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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