Software Development Engineer, Sponsored Products and Brands

Amazon Amazon · Big Tech · NY +1 · Software Development

Software Development Engineer II to design and build AI-powered advertiser controls, including bidding systems, agentic architectures, and experimentation systems. The role involves developing AI engineering infrastructure, interfacing agentic architectures, and designing experimentation systems to optimize ad campaigns on Amazon.

What you'd actually do

  1. Design and develop the Agentic platform using Gen AI/ML technologies to deliver low-latency, secure advertiser experiences
  2. Build scalable systems that process millions of data points and optimize for cost efficiency through resource utilization, token consumption, and memory management
  3. Develop conversational AI and natural language interactions for advertiser bidding guidance
  4. Collaborate with cross-functional teams to integrate AI-driven solutions across the advertising ecosystem
  5. Identify and eliminate root causes of operational issues with permanent fixes; proactively improve team operations, tooling, and processes

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language
  • Strong technical fluency in Generative AI, including a deep understanding of large language models (LLMs), model fine tuning, prompt engineering, Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG), AI model trade-offs (e.g., model size, latency, cost, and output quality).

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Master's degree or equivalent
  • Experience developing, deploying and managing AI products at scale

What the JD emphasized

  • AI engineering infrastructure
  • model fine-tuning
  • reinforcement learning
  • model inferencing
  • preference optimization
  • evaluation frameworks
  • agentic architectures
  • agent-to-agent communication protocols
  • lifecycle management for agent sessions
  • state management frameworks
  • multi-step workflows
  • experimentation systems
  • online experiments
  • conversational AI
  • natural language interactions
  • Generative AI
  • Large Language Models
  • LLMs
  • model fine tuning
  • prompt engineering
  • Reinforcement Learning from Human Feedback (RLHF)
  • Retrieval-Augmented Generation (RAG)

Other signals

  • AI-powered advertiser controls
  • design and build bidding controls and recommendations
  • AI engineering infrastructure
  • agentic architectures
  • conversational AI and natural language interactions