Staff / Principal Applied AI Researcher (Agentic Search)
Core
Designing and building agent-native search systems where AI agents actively plan, retrieve, evaluate, and reason over web data in real-time for machine consumption.
Role type
Staff/Principal Applied AI Researcher (Agentic Search)
Builds
Agent-native retrieval and ranking systems serving tens of thousands of production workloads
Domain
Cloud Infrastructure / Applied AI / Agentic Systems
Deliverable
production ML models
Required skills
Applied AI/ML system design, Search/retrieval/ranking algorithms, Deep learning (Transformers/Embeddings), LLM integration, Evaluation framework design, Python programming, Go/C++
Preferred skills
Large-scale search/recommendation systems, Agentic AI (tool use/autonomous workflows), RAG/multi-step retrieval, Publications/Open Source
Technologies
Python, Go, C++, Transformers, Embeddings, LLMs, Hybrid Search, Reranking
Responsibilities
Drive applied research and technical direction for retrieval and ranking systems, Design multi-stage retrieval architectures, Develop methods for grounding LLMs in real-time web data, Define evaluation paradigms for agentic systems, Lead experimentation on modern retrieval approaches, Analyse trade-offs across relevance/latency/cost, Deploy systems in high-throughput low-latency environments, Mentor engineers
Seniority
Senior/Principal, hands-on IC with mentorship
