Staff / Principal Applied ML Engineer (Search & Retrieval)

Why work at Nebius

Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.

Where we work

Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.

We are seeking a Staff / Principal Applied ML Engineer to design and own large-scale machine learning systems powering an agent-native search platform - the emerging web access layer for AI. You will operate end-to-end, from research-grade ideas to production systems, solving hard problems across retrieval, ranking, and indexing in high-throughput, low-latency environments.

Your responsibilities:

Own end-to-end ML systems from problem definition to production and iteration

Design and deploy models for retrieval, reranking, and search relevance

Build and optimise large-scale embedding-based retrieval and indexing systems

Develop query understanding, rewriting, and iterative retrieval pipelines

Drive measurable improvements in relevance, latency, and cost

Define and evolve evaluation frameworks and quality metrics

Operate systems under strict low-latency, high-throughput constraints

Collaborate closely with engineering and infrastructure teams to ship production systems

Contribute to system architecture and long-term technical direction

Raise the technical bar through leadership and mentorship

Must-haves:

8+ years of experience in software engineering or applied machine learning

Proven ownership of large-scale ML systems in production end-to-end

Strong programming skills in Python and at least one of Go or C++

Deep experience in one or more of: search, recommendation systems, ads ranking

Hands-on experience with retrieval, ranking, or matching systems

Experience operating high-throughput, low-latency production systems

Strong understanding of modern ML (embeddings, transformers, ranking models)

Experience designing evaluation frameworks and meaningful metrics

Ability to operate in ambiguity and drive problems end-to-end

Track record of making trade-offs between quality, latency, and cost

Nice to haves:

Experience with RAG, LLM-integrated systems, or agent-based architectures

Experience with large-scale indexing, crawling, or data pipelines

Familiarity with hybrid search (lexical and semantic)

Experience with personalization or user modelling

Contributions to open-source, publications, or technical talks

We conduct coding interviews as part of the process.