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Senior ML Engineer (Agentic Search)

Nebius Germany·Salary not specified

Primary stack

PythonGoC++Kubernetes

Nice to have's

KubernetesHugging Face

Job description

About the Position

Senior ML Engineer (Agentic Search)

Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; Zurich, Switzerland

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.

We are seeking a Senior ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. Our focus is agentic search, where AI systems plan, retrieve and refine rather than just return results.

You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time.

This is a high-impact role working on a production system used 24x7, operating at the scale of web search.

Responsibilities

  • Train and deploy ML models for retrieval, reranking, and search relevance, such as embedding models and cross-encoders, in production
  • Develop models supporting crawling, data selection, and content understanding
  • Build and maintain the data, training and evaluation pipelines behind our models: training-data generation, evaluation sets and retraining
  • Contribute to and improve our ML platform: training and orchestration workflows (Kubernetes, GPU scheduling, experiment tracking, model lifecycle) and inference optimisation for low-latency serving
  • Work closely with engineering to integrate and deploy ML models in production services, balancing latency, quality and cost
  • Apply the latest architectural advances and training procedures for retrieval and LLM-integrated systems

Requirements

  • 5+ years of experience in software engineering or applied machine learning
  • Strong programming skills in Python and at least one of Go or C++
  • Proven experience deploying ML models in production systems
  • Solid understanding of machine learning and modern deep learning, including transformers and embeddings
  • Experience building data or ML pipelines and working with workflow orchestration
  • Ability to define meaningful model metrics and evaluate models rigorously
  • Product-oriented mindset with a focus on impact and iteration

Nice to Have

  • Hands-on experience with retrieval, ranking, or recommendation
  • Experience with Kubernetes, distributed training or GPU scheduling, or inference optimisation (batching, quantisation, compilation)
  • Contributions to open-source projects, technical publications, or conference talks
  • Strong results on public benchmarks or leaderboards (e.g. MTEB, BEIR) or models released on Hugging Face

We Offer

  • 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

About the Company

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.

© Nebius Germany. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.

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