About this role
Developing and deploying machine learning models that power retrieval, ranking, and indexing at scale forms the core of daily operations in this position. Engineers create systems helping artificial intelligence platforms access fresh, reliable information in real time through an agent-native search platform. Nebius operates as a cloud infrastructure provider building a full-stack platform that supports developers from data training through to production deployment.
Operating within a fast-growing team in Zurich, the person in this role tackles challenges comparable to large-scale web search on a production system used around the clock. The group focuses on owning complex problems across compute, storage, networking, and applied artificial intelligence. Ideal candidates bring strong engineering foundations and a desire to build foundational web access layers for modern technology systems.
What you will do
- Design, train, and deploy machine learning models for retrieval, reranking, and search relevance in production environments.
- Build and optimize embedding-based indexing alongside large-scale retrieval systems.
- Develop models supporting crawling, data selection, and content understanding.
- Define and improve quality metrics for agent-native search while building robust evaluation pipelines.
- Operate systems at very large scale under high-throughput query workloads.
- Collaborate closely with engineering teams to integrate machine learning models into live production services.
- Analyze performance trade-offs across latency, quality, and cost.
- Experiment with and apply modern techniques in search, retrieval, and language model-integrated systems.
What you need
- Bring at least five years of professional experience in software engineering or applied machine learning.
- Demonstrate strong programming skills utilizing Python, Go, or C++.
- Show proven experience deploying machine learning models into production systems.
- Possess hands-on experience working with retrieval, ranking, recommendation, or similar machine learning problems.
- Maintain a strong understanding of machine learning and modern deep learning techniques.
- Display experience working with large-scale data systems and high-throughput environments.
- Exhibit the ability to design evaluation frameworks and define meaningful model metrics alongside a product-oriented mindset.
Nice to have
- 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 such as Kaggle or similar signals of strong technical ability
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
Skills and tech stack
Questions candidates ask
What is the interview process like?
The hiring process includes coding interviews as part of the evaluation. Candidates must also provide proof of employment eligibility as a condition of hire.
What kind of projects will I work on?
You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale for an agent-native search platform. This involves working on a production system used 24x7 and tackling challenges comparable to large-scale web search.
What benefits are provided?
The role offers competitive compensation alongside career growth and learning opportunities. Employees also receive flexibility and ownership within a collaborative and innovative culture.