Software Development Engineer, Trust Cx Innovations&ai Policy

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Software Development

Software Development Engineer II role focused on building foundational systems and consumer-facing features for trustworthy AI experiences at scale, emphasizing privacy-preserving AI architectures, responsible AI frameworks, and explainable AI systems for consumer devices like Alexa and Echo.

What you'd actually do

  1. Design and implement federated learning and differential privacy techniques for on-device and hybrid AI architectures
  2. Develop explainable AI interfaces that help customers understand how AI makes decisions
  3. Create low-latency evaluation systems that assess model outputs for safety, accuracy, and bias before reaching customers
  4. Architect on-device vs. cloud processing trade-offs that optimize for both privacy and performance
  5. Build privacy-preserving data pipelines that minimize data collection while maintaining model quality

Skills

Required

  • federated learning
  • differential privacy
  • privacy-preserving data pipelines
  • consent management frameworks
  • data minimization systems
  • explainable AI interfaces
  • transparency controls
  • privacy dashboards
  • identity management
  • on-device processing
  • cloud offloading
  • AI safety
  • accuracy
  • bias detection
  • multimodal AI systems
  • real-time evaluation
  • distributed systems
  • software development life cycle

Nice to have

  • Alexa
  • Echo
  • ambient computing
  • generative AI
  • LLM evaluation
  • model hallucinations

What the JD emphasized

  • privacy-preserving AI architectures
  • responsible AI frameworks
  • explainable AI systems
  • guardrails that protect customers
  • privacy-first design principles
  • Latency vs. Privacy Trade-offs
  • AI Safety at Scale
  • Ambient Computing Privacy
  • Multimodal AI Systems
  • Real-time Evaluation

Other signals

  • building foundational systems for trustworthy AI experiences
  • design and implement privacy-preserving AI architectures
  • responsible AI frameworks
  • explainable AI systems
  • guardrails that protect customers