Staff / Principal Applied AI Researcher (Agentic Search)

We are seeking a Staff or Principal Applied AI Researcher to join a fast-growing team building an agent-native search platform - the web access layer for AI systems.

You'll work on designing agent-native retrieval systems optimized for machine consumption rather than human search experience, building systems where LLMs iteratively plan, query, refine, and reason over results, and developing ranking and retrieval approaches for multi-step, agent-driven workflows under real-world constraints.

Key responsibilities:

• Drive applied research and technical direction across retrieval and ranking systems
• Design and evolve multi-stage retrieval architectures (query understanding, rewriting, reranking, iterative retrieval)
• Develop methods for grounding LLMs in real-time web data at scale
• Define and implement new evaluation paradigms and metrics for agentic systems
• Lead experimentation on modern retrieval approaches (embeddings, hybrid search, reranking) and bring them into production
• Analyze trade-offs across relevance, latency, and cost at scale
• Work closely with engineering teams to deploy systems in high-throughput, low-latency environments
• Own ambiguous problems end-to-end and contribute to product and research direction
• Mentor engineers and help raise the technical bar of the team