Applied Scientist Ii, Sponsored Products Autonomous Campaigns

Amazon Amazon · Big Tech · Palo Alto, CA · Machine Learning Science

The Applied Scientist II will pioneer agentic AI applications for Amazon advertisers, designing agentic architectures, developing tools and datasets, and building autonomous systems for campaign workflows. This role involves fine-tuning, reinforcement learning, preference optimization, and creating evaluation frameworks for safety and reliability. Responsibilities include designing and building agents, implementing optimization techniques, curating datasets, building evaluation pipelines with guardrails, developing agentic architectures with planning and tool use, and prototyping multi-agent orchestration. The role requires working independently on ambiguous problems and collaborating to bring innovations into production, staying current with LLM, RL, and agent-based AI research.

What you'd actually do

  1. Design and build agents for our autonomous campaigns experience.
  2. Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).
  3. Curate datasets and tools for MCP.
  4. Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails.
  5. Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning.

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

What the JD emphasized

  • autonomous campaigns
  • agentic AI applications
  • reason, plan, and act autonomously
  • fine-tuning, reinforcement learning, and preference optimization
  • evaluation frameworks
  • agent ecosystem
  • tool orchestration
  • multi-step reasoning
  • adaptive preference-driven behavior
  • ambiguous technical problems
  • bring innovative solutions into production
  • design and build agents
  • design and implement advanced model and agent optimization techniques
  • supervised fine-tuning, instruction tuning and preference optimization
  • curate datasets and tools
  • build evaluation pipelines for agent workflows
  • automated benchmarks, multi-step reasoning tests, and safety guardrails
  • develop agentic architectures
  • integrate planning, tool use, and long-horizon reasoning
  • prototype and iterate on multi-agent orchestration frameworks and workflows
  • translate findings into practical applications
  • generative AI technologies
  • re-inventing advertising experiences
  • bridging human creativity with artificial intelligence
  • transform every aspect of the advertising lifecycle
  • responsible and intelligent AI technologies
  • complex challenges
  • pushing the boundaries of what's possible with AI
  • highly personalized, context-aware campaign creation and management system
  • leverages LLMs together with tools
  • auction simulations
  • ML models
  • optimization algorithms
  • agentic framework
  • natural language queries
  • proactively delivering guidance
  • deep understanding of the advertiser
  • state-of-the-art agent architectures
  • tool integration
  • reasoning frameworks
  • model customization approaches
  • tuning, MCP, and preference optimization
  • scalable and adaptive

Other signals

  • design agentic architectures
  • develop tools and datasets
  • building systems that can reason, plan, and act autonomously
  • fine-tuning, reinforcement learning, and preference optimization
  • evaluation frameworks
  • agent ecosystem
  • tool orchestration
  • multi-step reasoning
  • adaptive preference-driven behavior
  • ambiguous technical problems
  • bring innovative solutions into production
  • design and build agents
  • design and implement advanced model and agent optimization techniques
  • supervised fine-tuning, instruction tuning and preference optimization
  • curate datasets and tools
  • build evaluation pipelines for agent workflows
  • automated benchmarks, multi-step reasoning tests, and safety guardrails
  • develop agentic architectures
  • integrate planning, tool use, and long-horizon reasoning
  • prototype and iterate on multi-agent orchestration frameworks and workflows
  • translate findings into practical applications
  • generative AI technologies
  • re-inventing advertising experiences
  • bridging human creativity with artificial intelligence
  • transform every aspect of the advertising lifecycle
  • responsible and intelligent AI technologies
  • complex challenges
  • pushing the boundaries of what's possible with AI
  • highly personalized, context-aware campaign creation and management system
  • leverages LLMs together with tools
  • auction simulations
  • ML models
  • optimization algorithms
  • agentic framework
  • natural language queries
  • proactively delivering guidance
  • deep understanding of the advertiser
  • state-of-the-art agent architectures
  • tool integration
  • reasoning frameworks
  • model customization approaches
  • tuning, MCP, and preference optimization
  • scalable and adaptive