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# Sr. Software Development Engineer, Guidance & Personalization

- **Company:** Amazon.com, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $184,900.0 - $250,200.0
- **Contract:** Internship / Graduate position
- **Skills:** A/B Testing, Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Code Review, Computer Programming, Software Design Patterns, Distributed Systems, Machine Learning, Recommender Systems, Software Engineering, Data Streaming, Large Language Models, Multi-Agent Systems, Information Technology, Build Process, Machine Learning Operations, Api Design, Software Coding, Software Version Control, Data Pipelines, Programming Languages, Microservices
- **Published:** July 1, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10462754/sr-software-development-engineer-guidance-personalization

## About the Role

Strong distributed systems fundamentals - you've built services that have to be both fast and correct under real traffic

 - Experience with ML infrastructure or production ML-adjacent systems; you understand what it takes to build reliable systems around probabilistic models
 - Product instincts - you care that the output is useful and legible to the person using it, not just that the pipeline ran successfully
 - Comfort with ambiguity - the team is working in a fast-moving space where some of the right architectural decisions are still being established
 - Strong written communication - our most consequential technical decisions live in design documents, and engineers are expected to author them, 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

Preferred Qualifications

 - 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

## Description

You'll work on systems that sit at the core of how advertisers understand and act on their advertising performance. The problems are technically hard:

 - Personalization at scale - building and maintaining structured models of advertiser behavior, goals, and preferences that update continuously across millions of accounts and inform real-time decisions
 - Real-time ML inference - systems that evaluate and rank recommendations, guidance, and actions at high volume with tight latency constraints across distributed infrastructure
 - Explainable AI systems - infrastructure that makes automated decisions auditable and legible to end users, not just to engineers. This includes stable data models for tracking decision inputs, surfacing confidence signals, and rendering explanations that advertisers can inspect and act on
 - Multi-agent coordination - APIs and contracts that allow multiple specialized AI agents to contribute outputs into a unified experience, with quality enforcement at the delivery layer
 - Event-driven data pipelines - high-volume ingestion, processing, and serving of advertising signals that feed both the ML models and the real-time recommendation systems

A day in the life
You'll work in a space where ML infrastructure, distributed systems, and product-quality UX concerns intersect daily.

Technology You'll Work With:

 - Large-scale distributed systems built on cloud infrastructure (microservices, event streaming, real-time serving)
 - Machine learning infrastructure - model serving, feature stores, A/B testing frameworks, online evaluation
 - Large language models and agentic AI systems - integrating LLM-powered agents into production advertising workflows, with the reliability and observability requirements that entails
 - High-volume data pipelines processing advertising signals across formats and surfaces
 - API design for multi-team and multi-agent consumption patterns


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