Responsibilities

• Develop scientific approaches to evaluate and improve LLM reasoning, grounding, retrieval, and agentic behaviors using offline benchmarks, online experimentation, and large-scale telemetry analysis.
• Drive complex technical investigations, model evaluations, and experimentation to improve quality, reliability, latency, cost efficiency, and user experience.
• Define and execute rigorous evaluation methodologies, offline benchmarks, online experimentation frameworks, and data-driven approaches for measuring model and product performance.
• Partner with engineering and product teams to translate research into scalable, production-ready systems used by millions of customers.
• Deeply analyze model behavior, failure modes, grounding effectiveness, reasoning quality, tool use, retrieval performance, and orchestration outcomes to identify opportunities for improvement.
• Develop approaches for agentic workflows, multi-step reasoning, planning, retrieval-augmented generation (RAG), tool orchestration, and reinforcement-learning-based optimization.
• Review technical designs, experimental results, and implementation approaches to ensure high standards of scientific rigor, reproducibility, an

Responsibilities

• Develop scientific approaches to evaluate and improve LLM reasoning, grounding, retrieval, and agentic behaviors using offline benchmarks, online experimentation, and large-scale telemetry analysis.
• Drive complex technical investigations, model evaluations, and experimentation to improve quality, reliability, latency, cost efficiency, and user experience.
• Define and execute rigorous evaluation methodologies, offline benchmarks, online experimentation frameworks, and data-driven approaches for measuring model and product performance.
• Partner with engineering and product teams to translate research into scalable, production-ready systems used by millions of customers.
• Deeply analyze model behavior, failure modes, grounding effectiveness, reasoning quality, tool use, retrieval performance, and orchestration outcomes to identify opportunities for improvement.
• Develop approaches for agentic workflows, multi-step reasoning, planning, retrieval-augmented generation (RAG), tool orchestration, and reinforcement-learning-based optimization.
• Review technical designs, experimental results, and implementation approaches to ensure high standards of scientific rigor, reproducibility, an

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