Staff Machine Learning Engineer, LLM Inference Optimization

Free resume; Berlin; Ibrido; EUR 150.000 - 190.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive compensation

Career growth and learning opportuniet

Descrizione del lavoro

Nebius is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable endpoints on Nebius' cloud platform.

The Staff Machine Learning Engineer will optimize components such as quantization, KV-cache, and engine selection, deploying and benchmarking diverse inference engines with researchers, kernel and infra engineers to reduce latency and cost per token.

Competenze

• 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.

Mansioni

• Own a platform-level optimization pillar (quantization, caching, or engine selection).
• Create evaluation and benchmarking frameworks across model families.
• Provide direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers.
• Optimize LLM/VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.
• Deploy and benchmark inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems.
• Build production-ready model-compression workflows (quantization, distillation, low-bit serving).
• Implement speculative decoding, KV-cache optimization, prefix caching, and continuous batching.

Conoscenze

Python

PyTorch

Latency optimization

Performance benchmarking

Communication

Collaboration

Strumenti

vLLM

SGLang

TensorRT-LLM

Triton Inference Server

NVIDIA Dynamo

Ray Serve

KServe

Descrizione del lavoro

Staff Machine Learning Engineer, LLM Inference Optimization

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.

• Competitive compensation
• Career growth and learning opportunitie