5 months ago
Remote, Worldwide +2 moreStaff+ / Senior
Responsibilities
- Drive applied research across retrieval, ranking, and agent-centric search systems.
- Design and improve multi-stage retrieval pipelines, including query understanding, rewriting, and reranking.
- Develop approaches for grounding LLMs using real-time web data.
- Define and implement evaluation methodologies and quality metrics for agent-native search.
- Lead experimentation on modern retrieval techniques such as hybrid search and embedding-based systems.
- Work closely with engineering teams to bring research into production at scale.
- Analyze trade-offs across relevance, latency, and cost in large-scale systems.
- Contribute to long-term research and product direction.
- Mentor engineers and researchers and raise the technical bar of the team.
Requirements
- 8+ years of experience in applied AI, machine learning, or software engineering.
- Strong track record of shipping ML or AI systems into production, not purely research.
- Deep experience in retrieval, ranking, search relevance, or recommendation systems.
- Strong understanding of modern deep learning approaches including transformers and embeddings.
- Experience working with LLM-integrated systems or knowledge-intensive AI applications.
- Hands-on experience designing evaluation frameworks and defining meaningful metrics.
- Strong programming skills in Python, Go, or C++.
- Ability to operate in a product-driven, fast-moving environment.
- Strong ownership and ability to drive ambiguous problems end-to-end.
Benefits
- Competitive salary and comprehensive benefits package.
- Opportunities for professional growth within Nebius.
- Flexible working arrangements.
- A dynamic and collaborative work environment that values initiative and innovation.
Categories
About Nebius
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.