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.

This is not traditional search. We are designing how AI agents - not humans - access, retrieve, and reason over information on the internet. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines.

This means solving retrieval and search problems under entirely new access patterns and scale.

Depending on your experience and scope, this role can be positioned at Staff or Principal level, with ownership over key parts of our applied AI research direction.

You will lead applied research that directly improves how AI systems retrieve, ground, and use real-world information in production, with research tightly coupled to large-scale deployment.

What you’ll work on

You will work on problems at the intersection of search, retrieval, and LLM-based systems, helping define how AI agents interact with the web.

This includes:
• Designing agent-native retrieval systems (not human search UX)
• Building systems where LLMs actively query, iterate, and reason over results
• Developing ranking and retrieval approaches optimized for agent workflows, not clicks
• Defining new evaluation paradigms for AI systems using the web

Your responsibilities

• 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

Must-haves

• 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)

Nice-to-haves

• 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