Global Optimization is a strategic initiative aimed at improving Amazon advertisers experience at global scale. We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows at global scale. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale.

Design and build agents that improve advertisers experiences globally

Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).

Design and implement optimization models that work at global scale taking into account nuances of multiple countries

Innovate new science models to help advertisers scale their campaigns globally

Curate datasets and tools for MCP.

Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails.

Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning.

Prototype and iterate on multi-agent orchestration frameworks and workflows.

Collaborate with peers across engineering and product to bring scientific innovations into production.

Stay current with the latest research in LLMs, RL, and agent-based AI, optimization and translate findings into practical applications.