Sr. Software Development Engineer, Devices & Services Trust CX Innovations

Amazon•Bellevue, WA

About The Position

Amazon's Devices & Services Trust CX Innovations team is building the future of responsible AI for consumer devices. We deliver privacy-first, accessible, and trustworthy AI experiences that millions of customers use daily across Amazon's device ecosystem—including Alexa, Echo, and other ambient computing products. Our mission is to ensure that as we push the boundaries of generative AI innovation, we maintain Amazon's high bar for customer trust, privacy, inclusion, and accessibility. 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.

Requirements

5+ years of non-internship professional software development experience
5+ years of programming with at least one software programming language experience
5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience as a mentor, tech lead or leading an engineering team
5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations, or experience leading engineering teams as a mentor or tech lead
5+ years of cloud based solution (AWS or equivalent), system, network and operating system experience
Customer Obsession for Trust: You deeply understand that trust is earned through consistent, transparent, and privacy-respecting experiences. You make technical decisions that prioritize customer trust even when it means additional complexity.
Technical Excellence: You have strong computer science fundamentals and practical experience building distributed systems. You write clean, maintainable code and establish best practices that enable your team to move fast without breaking things.
AI & Privacy Expertise: You understand both the capabilities and limitations of modern AI systems. You know how to implement privacy-preserving techniques without sacrificing user experience.
Bias for Action: You thrive in ambiguous environments where you need to make pragmatic trade-offs between innovation and safety. You ship iteratively and learn from customer feedback.
Collaboration & Communication: You partner effectively with product managers, UX designers, data scientists, and other engineers. You can explain complex technical concepts to non-technical stakeholders and influence architectural decisions across teams.
Inclusive Mindset: You design systems that work for all customers, considering diverse abilities, contexts, and use cases from the start rather than as an afterthought.

Nice To Haves

Master's degree in computer science or equivalent

Responsibilities

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
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
Implement WCAG 2.1 AA and Section 508 compliance for AI-powered interfaces across voice, visual, and multimodal experiences
Build accessible AI features that work seamlessly for customers with diverse abilities
Design inclusive AI systems that perform equitably across different demographics and use cases
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