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Senior Machine Learning Engineer, LLM Inference Optimization

Nebius· ML
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Palo Alto, CA$195.2k - $262.2kFull-timePosted 2mo ago

About the role

Senior ML Engineer focused on model and endpoint optimization for LLM/VLM inference, spanning model internals, quantization, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability.

Responsibilities

  • Own optimization projects for specific model families, customer endpoints, or serving backends.
  • Compare inference engines and recommend practical serving configurations suited to individual workloads.
  • Diagnose model-quality and performance regressions during production rollouts.
  • Improve LLM and VLM endpoint 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, and NVIDIA Dynamo.
  • Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery.
  • Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving (PDD). Evaluate KV-cache transfer, worker placement, and capacity balancing to determine workloads benefiting from the architecture.
  • Design and improve LLM request routers and scheduling policies balancing worker utilization, queuing, request characteristics, and KV-cache locality while meeting latency and reliability targets.
  • Scale dense and mixture-of-experts inference across multiple GPU nodes; tune tensor, pipeline, data, and expert parallelism (including WideEP) with hardware topology considerations.
  • Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token; validate architecture choices under representative traffic and GPU budgets.
  • Collaborate with GPU kernel engineers and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.
  • Produce design documents, performance reports, rollout plans, and customer-facing technical explanations.

Qualifications

  • Strong engineering skills in Python and PyTorch.
  • Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference.
  • Practical knowledge of at least one modern inference stack (vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent).
  • Strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving.
  • Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost.
  • Experience designing or optimizing distributed inference system architecture (e.g., request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism).
  • Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams.

Skills

Required:PythonPyTorchLLM/VLM inference optimizationInference stacksDistributed inference

Benefits

  • Health insurance: 100% company-paid medical, dental, and vision for employees and families.
  • 401(k) up to 4% company match with immediate vesting.
  • Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
  • Remote work reimbursement up to $85/month for mobile and internet.
  • Disability & life insurance: company-paid short-term, long-term and life insurance coverage.
Last updated Oct 7, 2026. Interested? Apply on the company site.
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