Senior ML Engineer, Agentic Search & Retrieval at Scale

Nebius Group; Greater London; Sur place; GBP 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

Competitive pay

Career growth

Flexibility

Collaborative culture

Impactful projects

International environment

Résumé du poste

Nebius is seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems. You will develop and deploy machine learning models powering retrieval, ranking, and indexing at scale, enabling real-time access to fresh information.

The role demands designing evaluation pipelines and working across latency, quality, and cost constraints. You will collaborate with engineering teams to integrate ML models into production services and tackle

Qualifications

• 5+ years of experience in software engineering or applied machine learning.
• Proven experience deploying ML models in production systems.
• Hands-on experience with retrieval, ranking, recommendation, or similar ML problems.
• Strong understanding of ML and modern deep learning techniques.

Responsabilités

• 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.
• Collaborate with engineering teams to integrate ML models into production services.
• Experiment with state-of-the-art techniques in search, retrieval, and LLM-integrated systems.

Connaissances

Python

Go

C++

ML in production

Retrieval/Ranking

Distributed systems

Outils

TensorFlow

PyTorch

Description du poste

Nebius is seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems. You will develop and deploy machine learning models powering retrieval, ranking, and indexing at scale, enabling real-time access to fresh information.

The role demands designing evaluation pipelines and working across latency, quality, and cost constraints. You will collaborate with engineering teams to integrate ML models into production services and tackle