Senior Machine Learning Engineer, LLM Inference Optimization
$195,200–$262,200 year
On-sitePalo Alto, California, United States
Job Summary
Own optimization work for specific model families, customer endpoints, or serving backends by running engine comparisons and recommending practical serving configurations. Debug model quality or performance regressions during production rollouts and optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, and continuous batching. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations.
Required Qualifications
- Strong Python and PyTorch engineering skills
- Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems
- Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems
- Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving
- Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs
- Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams
- Must be authorized to work in the country in which they apply
Desired Qualifications
- Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques
- Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods
- Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration
- CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role
- Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects
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