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 can think of this as Google for AI agents: a system designed for machines, not humans. We are building agentic search, where AI systems actively plan, retrieve, evaluate, and refine information rather than simply returning results. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines.
We are designing how AI agents - not humans - retrieve, evaluate, and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval and ranking under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data and serving tens of thousands of production workloads 24 by 7.
This role comes with ownership over key parts of our applied AI research direction and system design, with a strong expectation of defining new approaches and shipping measurable impact in production.
What you'll work on:
Designing agent native retrieval systems optimised for machine consumption rather than human search UX
Building systems where LLMs iteratively plan, query, refine, and reason over results
Developing ranking and retrieval approaches for multi step, agent driven workflows under real world constraints
Your responsibilites:
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, where correctness is not reducible to clicks
Lead experimentation on modern retrieval approaches (embeddings, hybrid search, reranking) and bring them into production
Analyse trade-offs across relevance, latency, and cost at scale
Work closely with engineering 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
Must haves:
8+ years of experience in applied AI, ML, or software engineering
Proven track record of shipping ML or AI systems to production at scale
Deep experience with search, retrieval, ranking, recommendation systems, or assistants
Strong understanding of modern deep learning, especially transformers and embeddings
Experience with LLM integrated or knowledge intensive systems
Experience designing evaluation frameworks and metrics for ML systems
Strong programming skills in Python and at least one of Go, C++, or similar
Ability to operate in a fast moving, product driven environment with high ownership and autonomy
Nice to haves
Experience with large scale search or recommendation systems
Background in agentic AI systems (agents, tool use, autonomous workflows)
Experience with RAG, multi step retrieval, or tool use
Publications, open source, or similar signals of technical depth and impact