Time since we first saw it
Day 125
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Senior ML Engineer, Search Optimization (Agentic Search)
Tel Aviv, Israel
Open 4 months, first seen by us 125 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign, implement and operate the retrieval system for a search vertical
Build knowledge-graph and entity-resolution layers, including entity linking and NER
Develop structured-extraction pipelines over messy, unstructured domain data
Model confidence in facts and how stale they are before serving them
Define relevance and quality metrics and drive measurable improvements
From the employer’s posting
Design, implement, and operate the retrieval system for a search vertical
Build knowledge-graph and entity-resolution layers: entity linking / NER
Develop structured-extraction pipelines over messy, unstructured domain data
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
About Nebius
Nebius is leading a new era in cloud infrastructure for the global AI economy.
In the employer’s words · Read in context
Posting record
Report an errorOpen 4 monthsWe first saw this posting 125 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 2, 2026125 days ago
- Recorded sightings
- 187
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Nebius is hiring
17 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring has slowed
- Its jobs tend to stay up a long time
- 12% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Neo4j
Where the posting names them
- 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
Your next step
Complete your application on careers.nebius.com. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
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.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- 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.