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Staff Machine Learning Engineer, LLM Inference Optimization

Nebius· ML
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Hybrid (Remote/Hybrid across listed locations)full-timePosted 4d ago

About the role

Role focused on production-grade inference optimization for open-source, customer, and internally optimized models, from model artifact to fast, reliable endpoints. Works at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment.

Responsibilities

  • Own a platform-level optimization pillar such as quantization, speculative decoding, KV-cache optimization, engine selection, or long-context serving.
  • Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families.
  • Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals.
  • 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 or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving.
  • Build reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token.
  • 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.

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.

Skills

Required:PythonPyTorchLLM/VLM inference optimization

Benefits

  • Competitive compensation
  • Career growth and learning opportunities
  • Flexibility and ownership
  • Collaborative and innovative culture
  • Opportunity to work on impactful AI projects
  • International environment and talented teams
Last updated Oct 21, 2026. Interested? Apply on the company site.
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