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Senior Machine Learning Engineer, LLM Inference Optimization

Nebius · Amsterdam, Netherlands

Confidence
Medium confidence
Posted
21 Oct 2026
First seen
24 Oct 2026
Last checked

Evidence

Medium confidence

Nebius is on the Dutch IND register. The posting doesn’t mention visas.

Sponsor register

Dutch IND public register of recognised sponsors

Listed as Nebius B.V.. KVK 51515539. Snapshot 26 Oct 2026.

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Job posting

Doesn’t mention visas or relocation.

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Seen on the job board

First seen 24 Oct 2026. We check every day and remove closed roles.

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Being on the register means Nebius can sponsor visas. It doesn’t confirm this role qualifies, so check with the recruiter.

About the role

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. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions.

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