Staff / Principal Applied AI Researcher – Agentic Search

• Drive applied research across retrieval, ranking, and agent-centric search systems
• Design and evolve multi-stage retrieval systems (query understanding, rewriting, reranking, iterative retrieval)
• Develop methods for grounding LLMs in real-time web data at scale
• Define evaluation frameworks and quality metrics for agent-native workloads
• Lead experimentation on modern retrieval approaches (hybrid search, embeddings, cross-encoders)
• Work closely with engineering to bring research into production in high-throughput systems
• Analyse trade-offs across relevance, latency, and cost at scale
• Contribute to long-term research and product direction
• Mentor engineers and researchers and raise the technical bar of the team

• 8+ years of experience in applied AI, ML, or software engineering
• Proven track record of shipping ML/AI systems into production at scale
• Deep experience in search, retrieval, ranking, recommendation systems, or assistants
• Strong understanding of modern deep learning (transformers, embeddings, ranking models)
• Experience working on LLM-integrated or knowledge-intensive systems
• Experience designing evaluation frameworks and metrics for ML systems
• Strong programming skills (Python + at least one of Go / C++ / similar)
• Ability to operate in a product-driven, fast-moving environment
• Strong ownership and ability to drive ambiguous problems end-to-end
• You have built or significantly contributed to large-scale production systems (search, recommendations, assistants, or similar)

• Experience with large-scale search systems (web search, ads, marketplaces, assistants)
• Background in agentic AI systems (coding agents, research agents, tool use)
• Experience with RAG systems, multi-step retrieval, and tool use
• Familiarity with query understanding, personalization, or recommendation systems
• Publications, conference talks, or open-source contributions