Software Development Engineer, Sponsored Products and Brands

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

Software Development Engineer II role focused on building AI-powered advertiser controls for Amazon's Sponsored Products and Brands. The role involves developing AI engineering infrastructure for bidding systems, including fine-tuning and inferencing, and interfacing agentic architectures for seamless AI system interaction. It also includes designing experimentation systems to test new bidding strategies and learning from advertiser behavior. The engineer will work on customer-facing products with direct business impact.

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

  • Generative AI
  • 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)
  • software development experience
  • design or architecture of new and existing systems
  • programming with at least one software programming language

Nice to have

  • distributed systems
  • cloud technologies
  • full software development life cycle
  • coding standards
  • code reviews
  • source control management
  • build processes
  • testing
  • operations
  • online advertising role
  • advertising products
  • ad serving technologies
  • 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
  • multi-step workflows
  • experimentation systems
  • Generative AI
  • Large Language Models
  • LLMs
  • model fine tuning
  • prompt engineering
  • Reinforcement Learning from Human Feedback (RLHF)
  • Retrieval-Augmented Generation (RAG)

Other signals

  • building AI engineering infrastructure
  • interface agentic architectures
  • design and build experimentation systems
  • develop conversational AI and natural language interactions