Applied Scientist Ii, Sponsored Products Global Optimization

Amazon Amazon · Big Tech · NY +1 · Applied Science

The Applied Scientist II role focuses on designing and building agentic AI applications for advertisers. This involves creating agentic architectures, developing tools and datasets, and building systems capable of reasoning, planning, and autonomous action. The role emphasizes fine-tuning, reinforcement learning, preference optimization, and creating evaluation frameworks for safety and reliability. It also involves advancing the agent ecosystem through experimentation with tool orchestration, multi-step reasoning, and adaptive preference-driven behavior, ultimately aiming to improve advertiser experiences and campaign optimization at a global scale.

What you'd actually do

  1. Design and build agents that improve advertisers experiences globally
  2. Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).
  3. Design and implement optimization models that work at global scale taking into account nuances of multiple countries
  4. Innovate new science models to help advertisers scale their campaigns globally
  5. Curate datasets and tools for MCP.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience in designing experiments and statistical analysis of results

Nice to have

  • Experience in professional software development
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience with LLMs, AI Agents, MCPs, Chain of Thought reasoning

What the JD emphasized

  • design agentic architectures
  • develop tools and datasets
  • building systems that can reason, plan, and act autonomously
  • fine-tuning
  • reinforcement learning
  • preference optimization
  • evaluation frameworks
  • tool orchestration
  • multi-step reasoning
  • adaptive preference-driven behavior
  • multi-agent orchestration frameworks

Other signals

  • design agentic architectures
  • develop tools and datasets
  • building systems that can reason, plan, and act autonomously
  • fine-tuning
  • reinforcement learning
  • preference optimization
  • evaluation frameworks
  • tool orchestration
  • multi-step reasoning
  • adaptive preference-driven behavior
  • multi-agent orchestration frameworks