6 months ago
Remote, EMEA +2 moreSenior
Responsibilities
- Design, train, and deploy machine learning models for retrieval, reranking, and search relevance in production.
- Build and optimize embedding-based indexing and large-scale retrieval systems.
- Develop models for crawling, data selection, and content understanding.
- Define and improve quality metrics and evaluation pipelines for agent-native search.
- Work on very large-scale systems with high-throughput query workloads.
- Collaborate with engineering teams to integrate machine learning models into production services.
- Analyze latency, quality, and cost trade-offs.
- Apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems.
- Contribute to product and architectural decisions.
Requirements
- At least 5 years of experience in software engineering or applied machine learning.
- Strong programming skills in Python, Go, or C++.
- Proven experience deploying machine learning models in production systems.
- Hands-on experience with retrieval, ranking, recommendation, or similar machine learning 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.
- Experience with search systems or large-scale information retrieval is preferred.
- Familiarity with embeddings, transformers, and modern NLP systems is preferred.
- Experience with LLM-powered or agent-based systems is preferred.
- Open-source contributions, technical publications, conference talks, or competitive ML experience such as Kaggle are preferred.
- Coding interviews are part of the hiring process.
Benefits
- Competitive salary and comprehensive benefits package.
- Flexible working arrangements.
- Opportunities for professional growth within Nebius.
- Dynamic and collaborative work environment.
Categories
About Nebius
Nebius builds a full-stack AI cloud offering GPU compute, storage, and tools for training and deploying ML models for startups, enterprises, and research labs. It sells consumption-based cloud infrastructure (IaaS/PaaS) and managed services tailored to generative AI workloads, including large-scale model training and inference. The company is headquartered in Amsterdam and operates as an independent provider.