Staff / Principal Applied AI Researcher (Agentic Search)

TL;DR

Staff / Principal Applied AI Researcher (Agentic Search): Building an agent-native search platform that enables AI systems to plan, retrieve, evaluate, and refine web information with an accent on retrieval, ranking, grounding, and production-scale reliability. Focus on designing multi-stage retrieval architectures, defining evaluation metrics beyond clicks, and balancing relevance, latency, and cost under high-throughput workloads.

Location: Remote within Europe. Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility.

Company

Cloud infrastructure and AI platform provider building full-stack solutions for data processing, model training, inference, and applied AI workloads.

What you will do

• Drive applied research and technical direction for retrieval and ranking systems.
• Design multi-stage retrieval architectures covering query understanding, rewriting, reranking, and iterative retrieval.
• Develop methods for grounding LLMs in real-time web data and enabling agentic planning and reasoning.
• Define evaluation paradigms and metrics for agentic systems where correctness cannot be measured by clicks alone.
• Lead experiments with embeddings, hybrid search, reranking, and related approaches through production deployment.
• Collaborate with engineering, mentor engineers, and guide product and research direction.

Requirements

• 8+ years of experience in applied AI, ML, or software engineering.
• Proven experience shipping ML or AI systems to production at scale.
• Deep expertise in search, retrieval, ranking, recommendation systems, or assistants.
• Strong understanding of modern deep learning, including transformers and embeddings, plus experience with LLM-integrated or knowledge-intensive systems.
• Experience designing evaluation frameworks and metrics for ML systems.
• Strong Python programming skills and proficiency in at least one of Go, C++, or a similar language.

Nice to have

• Experience with large-scale search or recommendation systems.
• Background in agentic AI, tool use, autonomous workflows, RAG, or multi-step retrieval.
• Publications, open-source contributions, or similar evidence of technical depth and impact.

Culture & Benefits

• Competitive compensation and career growth opportunities.
• Learning support, flexibility, ownership, and autonomy.
• Collaborative, innovative, and international environment.
• Opportunity to shape impactful AI products and work with experienced engineering and research teams.