Staff Machine Learning Engineer, LLM Inference Optimization

Nebius Group; Netherlands; On-site; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Career growth

Flexibility

Collaborative culture

Impactful AI projects

International environment

Job summary

Nebius is building production-grade inference for open-source, customer, and internal models. This role owns the path from model artifact to fast, reliable endpoints at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment.

You will optimize latency, throughput, memory usage, and cost per token; deploy engines such as vLLM, TensorRT-LLM, and Triton; and build reusable benchmarks and compression workflows with engineers and

Qualifications

• Strong Python and PyTorch programming capabilities for large-scale models.
• Hands-on experience deploying or optimizing LLM/VLM and high-throughput transformer inference systems.
• Experience with at least one modern inference stack (vLLM, SGLang, TensorRT-LLM, Triton, NVIDIA Dynamo, Ray Serve, or KServe).
• Deep understanding of transformer bottlenecks (KV cache, attention, memory bandwidth, batching, long-context serving).
• Ability to reason quantitatively about latency, throughput, quality, utilization, and cost.
• Strong communication and collaboration skills across teams.

Responsibilities

• Own platform-level optimization pillars (quantization, caching, engine selection, or long-context serving).
• Create reusable evaluation, benchmarking, and rollout frameworks for multiple model families.
• Provide technical direction to ML engineers, scientists, GPU kernel, and infra teams.
• Optimize LLM/VLM endpoints for latency, throughput, memory efficiency, GPU utilization, and cost per token.
• Deploy and benchmark inference engines such as vLLM, TensorRT-LLM, Triton, and related systems.
• Build production-ready model-compression workflows (quantization, distillation, low-bit serving).
• Develop reproducible benchmarks for TTFT, TPOT, tokens/sec per GPU, p95/p99 latency, and cost per token.

Skills

Python

PyTorch

LLM/VLM deployment

Inference optimization

Quantization techniques

Performance engineering

Technical communication

Tools

vLLM

SGLang

TensorRT-LLM

Triton Inference Server

NVIDIA Dynamo

Ray Serve

KServe

Job description

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 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.

Benefits & Perks:

• 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

Equal Opportunity Statement:

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

If you need accommodations during the application process, please let us know.