GPU Kernel Engineer – CUDA, Triton & Accelerator Performance

📋 Description

• Reviewing GPU and accelerator kernel implementations for correctness
• Comparing outputs against reference implementations
• Evaluating numerical tolerance thresholds
• Reviewing kernel benchmarks and determining whether comparisons are fair
• Identifying performance bottlenecks and optimization opportunities
• Assessing whether performance targets are realistic given hardware limits

🎯 Requirements

• 3+ years of hands-on experience developing, optimizing, or debugging GPU or accelerator kernels
• Strong experience with at least two of: CUDA, Triton, NKI / AWS Neuron, Pallas / JAX
• Strong understanding of GPU performance optimization
• Experience with kernel profiling tools such as Nsight, NCU, roofline analysis, or framework-native
• Understanding of memory bandwidth, compute throughput, GPU occupancy, shared memory, register
• Strong understanding of floating-point numerical correctness and tolerance thresholds