Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

You will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection.
You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality.
Design, implement, and operate the retrieval system for a search vertical

From the employer’s posting

As a Senior ML Engineer focused on Search Optimization, you will work on improving the quality of our search product across the all parts of the search stack. You will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection. Your goal will be to identify where search quality is lost, develop better approaches, and turn them into measurable improvements in production. This is an applied ML and information-retrieval role combining experimentation with production impact. You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality. In this position, your responsibility will be to

In this position, your responsibility will be to Design, implement, and operate the retrieval system for a search vertical Connect and tune the data pipeline, from ingestion to relevance tuning

Tools in this posting

Python
Neo4j

Source

- Experience building structured extraction over messy/unstructured domain data - Fluent in Python and comfortable with systems-level performance work Strong candidates may also have experience with:

- Connect and tune the data pipeline, from ingestion to relevance tuning - Build knowledge-graph and entity-resolution layers: entity linking / NER, ontologies, and graph databases (Neo4j or similar) - Develop structured-extraction pipelines over messy, unstructured domain data

Strong candidates may also have experience with: - Knowledge graphs: entity resolution, entity linking / NER, graph DBs (Neo4j), ontologies / schema design - Owning relevance / ranking for a real product and improving it against IR metrics

About Nebius

Nebius is leading a new era in cloud infrastructure for the global AI economy.

In the employer’s words · Read in context

Job description

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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 Product

In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.

We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale.

The Role

As a Senior ML Engineer focused on Search Optimization, you will work on improving the quality of our search product across the all parts of the search stack.

You will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection. Your goal will be to identify where search quality is lost, develop better approaches, and turn them into measurable improvements in production.
This is an applied ML and information-retrieval role combining experimentation with production impact. You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality.

In this position, your responsibility will be to

Design, implement, and operate the retrieval system for a search vertical
Connect and tune the data pipeline, from ingestion to relevance tuning
Build knowledge-graph and entity-resolution layers: entity linking / NER, ontologies, and graph databases (Neo4j or similar)
Develop structured-extraction pipelines over messy, unstructured domain data
Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it
Define evaluation and quality metrics for relevance and drive measurable improvements
Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met
Enable safe experimentation with retrieval, ranking, and extraction strategies

You may be a good fit if you have:

6+ years of software engineering experience, some of it in search / information retrieval
Strong IR fundamentals: inverted indexes, BM25/TF-IDF, query understanding, ranking, and evaluation (nDCG/MRR/recall@k)
Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models
Experience building structured extraction over messy/unstructured domain data
Fluent in Python and comfortable with systems-level performance work

Strong candidates may also have experience with:

Knowledge graphs: entity resolution, entity linking / NER, graph DBs (Neo4j), ontologies / schema design
Owning relevance / ranking for a real product and improving it against IR metrics
Data quality, truth discovery, or systems that decide how much to trust a piece of information
Published work on IR, ranking, or knowledge graphs

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.

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Source & posting history

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Source excerpts

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Pay: No pay amount identified in the saved description.
Location & working pattern: Tel Aviv, Israel
- Strong IR fundamentals: inverted indexes, BM25/TF-IDF, query understanding, ranking, and evaluation (nDCG/MRR/recall@k) - Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models - Experience building structured extraction over messy/unstructured domain data
Work authorization: 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.

Open source at careers.nebius.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Jul 2, 2026
Recorded sightings: 110
Last seen by us: Oct 12, 2026

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Senior ML Engineer, Search Optimization (Agentic Search)