Machine Learning Engineer, LLM Inference Optimization

Nebius Group; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation

Career growth

Flexibility and ownership

Collaborative culture

International environment

Job summary

Nebius ищет инженера ML/AI для разработки и оптимизации инструментов инференса в облаке. Вы будете работать над End-to-End цепочкой: от моделей до сервисов, уделяя внимание задержке, пропускной способности и расходам.

В роли предполагается работа с современными стеками (vLLM, TensorRT-LLM, Triton) и тесное сотрудничество с исследователями, инженерами ядра и продакт-менеджерами. Ожидается активное участие в открытых проектах и возможностях роста.

Qualifications

• Глубокие знания Python и PyTorch.
• Опыт развёртывания или оптимизации систем высокопоточного вывода.
• Знание стеков vLLM / TensorRT-LLM / Triton / KServe.
• Понимание узких мест трансформер-вычислений.
• Умение анализировать латентность, пропускную способность и стоимость.

Responsibilities

• Владение pillar оптимизации уровня платформы (quantization, KV-cache и др.).
• Создание повторяемых оценочных, бенчмаркинговых и развёртывочных фреймворков.
• Давать техническое направление MLEs, Applied Scientists, GPU kernel инженерам.
• Оптимизация LLM/VLM эндпоинтов по задержке, пропускной способности и памяти.
• Развертывание/настройка инференс-движков (vLLM, SGLang, TensorRT-LLM, Triton Server).
• Производство рабочих процессов с квантованием, distillation и т.д.
• Писать дизайн-документы и отчёты о производительности и развёртываниях.

Skills

Python

PyTorch

Latency optimization

Quantitative reasoning

Tools

vLLM

TensorRT-LLM

Triton

KServe

Job description

Описание:

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. 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.

Задачи:

• 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.

Требования:

• 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.
• 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.

Условия:

• 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

What's it like to work at Nebius:

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI