Member of Technical Staff, Kernel Engineer

NeuroSpark builds and operates a high-performance AI inference platform that helps enterprises run large language models faster, cheaper, and at scale. The Member of Technical Staff, Kernel Engineer will design and optimize GPU and accelerator kernels, identify production performance bottlenecks, and integrate hardware-aware improvements into inference runtimes and distributed serving systems.

Responsibilities:

• Design, implement, and optimize high-performance GPU kernels for performance-critical AI operations, including GEMM, attention, normalization, quantization, KV-cache operations, and Mixture-of-Experts (MoE) workloads
• Develop and optimize kernels using technologies such as CUDA, Triton, C++, PTX, CUTLASS, and related GPU programming frameworks
• Profile production inference workloads to identify bottlenecks across compute, memory bandwidth, memory hierarchy, kernel launch overhead, synchronization, and data movement
• Optimize GPU execution through techniques including memory coalescing, shared-memory utilization, tiling, warp-level programming, Tensor Core utilization, operator fusion, latency hiding, and compute/communication overlap
• Improve end-to-end LLM inference performance across latency, throughput, memory utilization, concurrency, and hardware efficiency, rather than optimizing kernels in isolation
• Develop and optimize kernels for modern model architectures, including Transformer-based LLMs, attention variants, MoE models, and emerging model architectures
• Implement and evaluate lower-precision execution and quantization strategies, including FP16, BF16, FP8, FP4, INT8, and other hardware-supported formats
• Integrate optimized kernels and operators into inference frameworks and internal runtimes built around technologies such as PyTorch, Triton, vLLM, SGLang, TensorRT-LLM, or equivalent systems
• Use profiling and performance-analysis tools such as Nsight Systems, Nsight Compute, PyTorch Profiler, roofline analysis, and internal benchmarking infrastructure to diagnose and resolve performance regressions
• Build reliable benchmarks, correctness tests, and performance regression tests to ensure kernel improvements remain numerically correct and production-ready
• Optimize workloads across multi-GPU and distributed environments, working with the broader infrastructure team on communication, parallelism, and compute efficiency
• Help extend NeuroSpark's inference stack across heterogeneous hardware, including NVIDIA GPUs, AMD GPUs, and other current and emerging AI accelerators
• Work closely with hardware vendors, inference engineers, and distributed-systems engineers to evaluate new accelerator architectures and translate hardware capabilities into production performance improvements
• Contribute to architectural decisions affecting the Company's inference runtime, model execution layer, and hardware-performance roadmap