Senior ML Engineer (Agentic Search)
Salary not listed • Remote • Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; Zurich, Switzerland • Full-time
Posted 15d ago
Job Location
Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; Zurich, Switzerland
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Nebius is building a full-stack AI cloud platform for the global AI economy, supporting developers and enterprises from data and model training through to production deployment. We are seeking a Senior ML Engineer to join a fast-growing team focused on an agent-native search platform for AI systems. This role involves developing and deploying machine learning models for retrieval, ranking, and indexing at scale, enabling AI systems to access real-time information. It's a high-impact position on a production system operating at web search scale.
Responsibilities
- Train and deploy ML models for retrieval, reranking, and search relevance, including embedding and cross-encoder models.
- Develop models for crawling, data selection, and content understanding.
- Build and maintain data, training, and evaluation pipelines for models, including data generation and retraining.
- Contribute to and improve the ML platform, covering training/orchestration workflows and inference optimization.
- Collaborate with engineering to integrate and deploy ML models in production services, balancing performance and cost.
- Apply advanced architectural and training procedures for retrieval and LLM-integrated systems.
Requirements
- 5+ years of experience in software engineering or applied machine learning.
- Strong Python programming skills and proficiency in Go or C++.
- Proven experience deploying ML models in production systems.
- Solid understanding of machine learning, deep learning, transformers, and embeddings.
- Experience building data or ML pipelines and using workflow orchestration tools.
- Ability to define model metrics and conduct rigorous model evaluation.
- Product-oriented mindset with a focus on impact and iteration.
- Hands-on experience with retrieval, ranking, or recommendation systems.
- Experience with Kubernetes, distributed training, GPU scheduling, or inference optimization.
- Contributions to open-source projects, technical publications, or conference talks.
- Strong performance on public benchmarks or leaderboards, or models released on Hugging Face.
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