Staff / Principal Applied AI Researcher (Agentic Search)

Role description

Role Overview

Join a fast-growing team building an agent native search platform, 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.

What You Will Do

Designing agent native retrieval systems, 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.

Why It Might Be a Fit

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. You will drive applied research and technical direction across retrieval and ranking systems, design and evolve multi-stage retrieval architectures, and develop methods for grounding LLMs in real-time web data at scale.

Requirements

• 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

Benefits

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams