Senior ML Engineer, Search Optimization (Agentic Search)
Salary not listed • Tel Aviv, Israel • Full-time
Posted 4mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Nebius is building an agent-native search platform designed for AI systems, providing programmatic, low-latency search APIs for retrieving, filtering, and reasoning over real-world information at scale. As a Senior ML Engineer focused on Search Optimization, you will be instrumental in enhancing the quality of our search product across the entire stack. This applied ML and information-retrieval role involves identifying and addressing areas where search quality is lost, developing innovative approaches, and implementing them to achieve measurable improvements in production. You will work with real-world queries, large-scale search systems, and evaluation signals to boost relevance, recall, freshness, and overall result quality.
Responsibilities
- 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, including entity linking/NER, ontologies, and graph databases.
- Develop structured-extraction pipelines over messy, unstructured domain data.
- Model and reason about data freshness and trust before serving information.
- Define evaluation and quality metrics for relevance and drive measurable improvements.
- Collaborate with crawling, indexing, and ML teams to meet retrieval and ranking requirements.
- Enable safe experimentation with retrieval, ranking, and extraction strategies.
Requirements
- 6+ years of software engineering experience, with some 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.
Benefits
- 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