Staff Applied AI Researcher (Agentic Search)
Core
Designing an agent-native search system that handles low-latency single-hop search to complex deep search tasks requiring iterative planning, querying, refinement, and reasoning for AI agents.
Role type
Staff Applied AI Researcher (Agentic Search)
Builds
Agent-native search platform serving tens of thousands of production workloads 24/7
Domain
AI infrastructure, agentic systems, search, retrieval, ranking
Deliverable
production ML models
Required skills
applied AI, ML, software engineering, search, retrieval, ranking, RAG-based assistants, deep learning, transformers, embeddings, LLM-integrated systems, evaluation frameworks, Python
Preferred skills
agentic AI systems, RL post-training of LLMs, VLMs
Technologies
Python, transformers, embeddings, LLMs
Responsibilities
Drive applied research and technical direction across retrieval and ranking systems; Design and evolve multi-stage retrieval architectures; Lead experimentation on modern retrieval approaches and bring them into production; Develop methods for grounding LLMs in real-time web data at scale; Partner with the evaluation team to define new metrics for agentic systems; Analyze trade-offs across relevance, latency, and cost; Mentor researchers and engineers
Seniority
Staff, hands-on IC with technical direction
