Staff / Principal Applied AI Researcher (Agentic Search)

Salary

Core Qualifications

Preferred Qualifications

Key Responsibilities

• •Drive applied AI research and technical direction across retrieval and ranking systems for agent-native search.
• •Design and evolve multi-stage retrieval architectures, including query understanding, query rewriting, reranking, and iterative retrieval.
• •Develop approaches for grounding LLMs in real-time web data while maintaining quality, scalability, and reliability.
• •Build and refine systems where LLMs can plan, query, evaluate, refine, and reason over retrieved information across multi-step workflows.
• •Define new evaluation frameworks, metrics, and experimentation methodologies for agentic systems.
• •Lead experimentation with modern retrieval technologies, including embeddings, hybrid search, reranking, and related approaches, and transition successful methods into production.
• •Analyze and manage trade-offs between relevance, latency, reliability, and infrastructure cost at scale.
• •Partner closely with engineering teams to deploy AI and retrieval systems in high-throughput, low-latency production environments.
• •Take ownership of ambiguous and complex technical problems from research through implementation and contribute to broader product and research direction.
• •Mentor engineers, share technical expertise, and help raise the research and engineering standards of the team.