Senior Applied ML Engineer (Agentic Search)

Nebius Group; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning opportunit(

Flexibility and ownership

Collaborative and innovative culture

Opportunity to work on impactful AI (

Job summary

Nebius is building a full-stack AI cloud platform to support developers and enterprises from data and model training to production deployment. We seek a Senior Applied ML Engineer to develop and deploy ML models powering retrieval, ranking, and indexing at scale, in a fast-moving, production environment.

You will work on high-impact systems with 24x7 availability, tackle challenges comparable to large-scale web search, and collaborate with engineers to integrate models into services across the

Qualifications

• 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.
• Ability to design evaluation frameworks and metrics for models.

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 evaluation pipelines.
• Work on systems operating at very large scale with high-throughput queries.
• Collaborate closely with engineering teams to integrate ML models into production services.

Skills

Python

Go

C++

ML deployment

Retrieval/Ranking

Job description

About Nebius:

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.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

We are seeking a 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's it 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.