Staff Machine Learning Engineer, LLM Inference Optimization

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Role Summary Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment. What You Will Do • 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. 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. Preferred 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 or