AI Performance Engineer

Benchmark AI models (LLMs, vision, multimodal) across hardware configurations; measure latency, throughput, utilization, memory behavior, and scaling efficiency Profile workloads end-to-end using tools such as Nsight Systems/Compute, PyTorch Profiler, and system telemetry (nvidia-smi, DCGM) to isolate bottlenecks Build roofline/performance models to quantify achieved vs. theoretical performance and prioritize the highest-impact optimizations Apply and evaluate optimizations: quantization, pruning, distillation, operator/kernel fusion, graph compilation, KV-cache management, batching strategies, speculative decoding Recommend hardware/system configurations (GPU selection, memory sizing, interconnect, storage/network I/O) for given model workloads Establish performance baselines, SLAs, and regression testing so models stay fast as they evolve Write clear analyses and recommendations for engineering and leadership audiences

BS/MS in CS, Computer Engineering, EE, or equivalent practical experience Strong Python; working proficiency in at least one systems language (C++/Rust/C) Hands-on experience with a deep-learning framework (PyTorch preferred), including model execution, export, and profiling Demonstrated experience delivering measurable performance improvements in DL training or inference Solid grounding in computer architecture: memory hierarchy, bandwidth vs. compute limits, parallelism Ability to reason quantitatively about latency, throughput, batching, memory footprint, and utilization under real workloads Fluency with Linux and GPU computing environments Preferred qualifications GPU programming (CUDA, Triton, ROCm/HIP) and low-level libraries (cuBLAS, cuDNN, CUTLASS) Inference runtimes/serving engines: TensorRT(-LLM), ONNX Runtime, vLLM, SGLang, Triton Inference Server LLM inference mechanics: attention, KV caching, prefill vs. decode, continuous batching, speculative decoding Distributed training/inference: data/tensor/pipeline parallelism, NCCL, InfiniBand/RoCE Model compression research or MLPerf-style benchmarking experience Edge/on-device deployment (Jetson, NPUs, Core ML) if your systems include e