Primary Skills: CUDA programming (advanced), Triton optimization (advanced), Profiling tools (advanced), Debugging kernels (advanced), Performance tuning (advanced)
Contract Type: W2
Duration: 3+ months with possible extension or conversion
Location: Bellevue, WA ()
Pay Range: $85.00 - $90.00 Per hour on W2
#LP
Job Summary: This role involves the low-level development and optimization of compute kernels for AI accelerator hardware. You will ensure training environments accurately mirror real-world kernel engineering challenges by writing, debugging, and optimizing operators. A key aspect of this position is using profiling tools and accelerator programming interfaces to validate task realism and define solution quality.
Key Responsibilities: - Develop and optimize custom compute kernels for AI accelerators.
- Validate training environment tasks for realistic kernel development scenarios.
- Debug and profile kernel code to identify and resolve performance issues.
- Analyze kernel performance against hardware constraints and memory hierarchy.
- Consult with training environment development teams on task realism and quality.
Must-Have Skills: - Computer Science, Computer Engineering, Electrical Engineering, or equivalent technical degree and 7+ years of relevant experience is required.
- Direct experience with AWS Trainium/Inferentia and the Neuron Kernel Interface (NKI) and Hands-on proficiency with a low-level kernel programming language such as CUDA, Triton, or the AWS Neuron Kernel Interface (NKI).
- Familiarity with the Neuron SDK, Neuron Profiler, and framework integration (PyTorch, JAX).
- Experience porting kernels between accelerator backends (e.g., CUDA/Triton to NKI) and u nderstanding of deep learning operator implementations (attention, GEMM, normalization).
Industry Experience:
Experience in developing and optimizing low-level code for AI accelerators is essential, with a strong preference for experience with Triton over CUDA.
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