Copy of Staff / Principal Applied AI Researcher (Agentic Search)
Core
Designing and building an agent-native search platform that enables AI systems to actively plan, retrieve, evaluate, and refine information in real-time for machine consumption.
Role type
Staff/Principal Applied AI Researcher (Agentic Search)
Builds
Agent-native retrieval systems, multi-stage retrieval architectures, and ranking approaches for LLM-driven workflows.
Domain
Applied AI, Agentic AI, Search & Retrieval, Large Language Models
Deliverable
production ML models
Required skills
Applied AI/ML system design, Search/retrieval/ranking expertise, Deep learning (transformers/embeddings), LLM integration, Evaluation framework design, Python programming, Go/C++
Preferred skills
Large-scale search/recommendation systems, Agentic AI systems, RAG/multi-step retrieval, Technical publications
Technologies
Python, Go, C++, Transformers, Embeddings, LLMs
Responsibilities
Drive applied research and technical direction for retrieval and ranking systems; Design and evolve multi-stage retrieval architectures; Develop methods for grounding LLMs in real-time web data; Define and implement new evaluation paradigms for agentic systems; Lead experimentation on modern retrieval approaches and bring them to production; Analyse trade-offs across relevance, latency, and cost; Mentor engineers and raise the technical bar of the team.
Seniority
Staff/Principal, hands-on IC with strategic ownership
