Sr. Software Engineer, Trust CX Innovations & AI Policies
Amazon.com
We are seeking an exceptional Software Development Engineer III to build the foundational systems and consumer-facing features that enable trustworthy AI experiences at scale. You will partner closely with our Product Manager-Technical to design and implement privacy-preserving AI architectures, responsible AI frameworks, and accessibility features that set the standard for consumer AI products.
This is a high-impact role where you'll tackle complex technical challenges at the intersection of AI innovation and customer trust—balancing performance with privacy, building explainable AI systems, and creating guardrails that protect customers while enabling delightful experiences.
Key job responsibilities
What You'll Build
Privacy-Preserving AI Systems:
* Design and implement federated learning and differential privacy techniques for on-device and hybrid AI architectures
* Build privacy-preserving data pipelines that minimize data collection while maintaining model quality
* Create consent management frameworks and data minimization systems that give customers control
* Architect on-device vs. cloud processing trade-offs that optimize for both privacy and performance
Responsible AI Infrastructure:
* Develop AI evaluation frameworks to measure model quality, safety, and bias across diverse customer populations
* Implement guardrails and safety boundaries for LLMs and foundation models in consumer applications
* Build observability and monitoring systems for AI performance, hallucination detection, and trust metrics
* Create human-in-the-loop review systems and escalation pathways for edge cases
Consumer-Facing Trust Features:
* Develop explainable AI interfaces that help customers understand how AI makes decisions
* Build transparency controls that show customers what data is used and how
* Create privacy dashboards and settings that give customers meaningful control over their AI experiences
* Implement cross-device identity management with privacy-first design principles
Key Technical Challenges
* Latency vs. Privacy Trade-offs: Optimize time-to-first-token and response times while maintaining strong privacy guarantees through on-device processing and selective cloud offloading
* AI Safety at Scale: Reduce hallucinations to
This is a high-impact role where you'll tackle complex technical challenges at the intersection of AI innovation and customer trust—balancing performance with privacy, building explainable AI systems, and creating guardrails that protect customers while enabling delightful experiences.
Key job responsibilities
What You'll Build
Privacy-Preserving AI Systems:
* Design and implement federated learning and differential privacy techniques for on-device and hybrid AI architectures
* Build privacy-preserving data pipelines that minimize data collection while maintaining model quality
* Create consent management frameworks and data minimization systems that give customers control
* Architect on-device vs. cloud processing trade-offs that optimize for both privacy and performance
Responsible AI Infrastructure:
* Develop AI evaluation frameworks to measure model quality, safety, and bias across diverse customer populations
* Implement guardrails and safety boundaries for LLMs and foundation models in consumer applications
* Build observability and monitoring systems for AI performance, hallucination detection, and trust metrics
* Create human-in-the-loop review systems and escalation pathways for edge cases
Consumer-Facing Trust Features:
* Develop explainable AI interfaces that help customers understand how AI makes decisions
* Build transparency controls that show customers what data is used and how
* Create privacy dashboards and settings that give customers meaningful control over their AI experiences
* Implement cross-device identity management with privacy-first design principles
Key Technical Challenges
* Latency vs. Privacy Trade-offs: Optimize time-to-first-token and response times while maintaining strong privacy guarantees through on-device processing and selective cloud offloading
* AI Safety at Scale: Reduce hallucinations to
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