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# ML Engineer

- **Company:** Nebius
- **Location:** Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Automated Storage and Retrieval Systems, Big Data, C++ (Programming Language), Computer Programming, Content Analysis, Information Retrieval, Python (Programming Language), Machine Learning, Software Engineering, Evaluation Pipelines, Large Language Models, Deep Learning, Kaggle, Agentic-AI, Machine Learning Operations, Data Selection
- **Published:** October 18, 2026
- **Apply:** https://startup.jobs/senior-ml-engineer-agentic-search-nebius-10493867

## About the Role

* 5+ years of experience in software engineering or applied machine learning
 * Strong programming skills in Python, Go, or C++
 * Proven experience deploying ML models in production systems
 * Hands-on experience with retrieval, ranking, recommendation, or similar ML problems
 * Strong understanding of machine learning and modern deep learning techniques
 * Experience working with large-scale data systems and high-throughput environments
 * Ability to design evaluation frameworks and define meaningful model metrics
 * Product-oriented mindset with a focus on impact and iteration
 * Strong problem-solving skills and ability to work in a distributed team

Nice-to-haves:

 * Experience with search systems or large-scale information retrieval
 * Familiarity with embeddings, transformers, and modern NLP systems
 * Experience working on LLM-powered or agent-based systems
 * Contributions to open-source projects, technical publications, or conference talks
 * Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability, 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.

## Description

* Design, train, and deploy ML models for retrieval, reranking, and search relevance in production
 * Build and optimise embedding-based indexing and large-scale retrieval systems
 * Develop models supporting crawling, data selection, and content understanding
 * Define and improve quality metrics for agent-native search and build evaluation pipelines
 * Work on systems operating at very large scale, including high-throughput query workloads
 * Collaborate closely with engineering teams to integrate ML models into production services
 * Analyse performance trade-offs across latency, quality, and cost
 * Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems
 * Contribute to product and architectural decisions in a fast-moving environment