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1–2 jobs· newest first

Senior Staff Machine Learning Scientist, Search
SiriusXM · Dublin, Ireland
Full-time · Staff
Senior ML Engineer, Search Optimization (Agentic Search)
Tavily · Tel Aviv, Israel
Senior

+10K new in the last hour

Senior Staff Machine Learning Scientist, Search

SiriusXM · Posted Nov 9, 2026

Dublin, Dublin, Ireland
Full-time · Staff
8+ years

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Senior ML Engineer, Search Optimization (Agentic Search)

Tavily · Posted Nov 4, 2026

Tel Aviv, Tel Aviv, Israel
Senior
6+ years

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AI

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Technical leader responsible for advancing the long-term direction of search and discovery capabilities across the audio ecosystem, focusing on retrieval, ranking, query understanding, and AI-powered search.

Responsibilities

Lead the design, development, and evolution of advanced machine learning systems supporting search relevance, retrieval, ranking, query understanding, and personalization.
Define technical approaches and drive execution for highly complex search and discovery initiatives.
Develop and improve transformer-based, deep learning, semantic retrieval, and generative AI solutions.
Establish and refine methodologies for search evaluation, experimentation, offline relevance measurement, and online testing.
Partner closely with Product, Engineering, Data Science, Analytics, and Content teams.

Requirements

M.S. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, or a related quantitative field required
Ph.D. preferred

Skills

Machine learning Information retrieval Search relevance Ranking systems Semantic search Query understanding Transformer architectures Generative AI Production ML Large language models Recommendation systems

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AI

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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. This is an applied ML and information-retrieval role combining experimentation with production impact.

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: entity linking / NER, ontologies, and graph databases (Neo4j or similar)
Develop structured-extraction pipelines over 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

Requirements

Nice to have: BM25
Nice to have: TF-IDF
Nice to have: nDCG
Nice to have: MRR
Nice to have: recall@k

Skills

Information Retrieval Machine Learning Query understanding Ranking Reranking Vector search Hybrid retrieval Embeddings Python Neo4j

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