Senior Applied ML Engineer (Agentic Search)

Nebius B.V.; Deutschland; Remote; EUR 110.000 - 160.000; Vollzeit

Zusammenfassung

Nebius is building an agent-native search platform powering AI systems, delivering real-time access to fresh information at scale. You will design, train, and deploy ML models for retrieval, ranking, and indexing in production to enable high-quality AI-powered search.

The role demands deep ML expertise at scale, strong Python/Go/C++ coding, and a product-driven mindset to ship low-latency, high-impact solutions in a fast-moving team.

Qualifikationen

• 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, or similar ML problems.
• Strong understanding of machine learning and modern deep learning techniques.
• Experience 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.

Aufgaben

• 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

Kenntnisse

5+ years of experience in software eng

Applied ML experience

Strong problem-solving

Distributed teamwork

Product-oriented mindset

Evaluation framework design

High-throughput environments

Tools

Python

Go

C++

Jobbeschreibung

Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. 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, tackling challenges comparable to large-scale web search.

Your responsibilities:

• 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

Must-haves:

• 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

We conduct coding interviews as part of the process.

Benefits & Perks:

• 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

What it's like to work at Nebius:

• Fast moving
• Bold thinking
• Constant growth
• Meaningful impact
• Trust and real ownership
• Opportunity to shape the future of AI

Equal Opportunity Statement:

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. 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