Staff ML Engineer: Inference & Optimization Lead

Resume Skip; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning opportunity

Flexibility and ownership

Collaborative and innovative culture

Impactful AI projects

International environment

Job summary

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, sitting at the intersection of model internals, inference engines, serving architecture, benchmarking, and deployment.

You will drive platform-level optimizations and build reusable evaluation and rollout frameworks across model families, collaborating with MLEs, engineers, and researchers to meet strict latency and

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.

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.

Skills

Python

PyTorch

LLM/VLM inference

Performance optimization

Quantization

Benchmarking

Cross-team collaboration

Tools

vLLM

SGLang

TensorRT-LLM

Triton Inference Server

NVIDIA Dynamo

Ray Serve

KServe

Job description

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, sitting at the intersection of model internals, inference engines, serving architecture, benchmarking, and deployment.

You will drive platform-level optimizations and build reusable evaluation and rollout frameworks across model families, collaborating with MLEs, engineers, and researchers to meet strict latency and