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# Principal Machine Learning Engineer

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $101,000.0 - $138,600.0
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Distributed Computing Environment, Machine Learning, Open Source Technology, Tensorflow, Large Language Models, Deep Learning, Machine Learning Operations
- **Published:** August 10, 2026
- **Apply:** https://www.careerjet.com/job/us16a631022b44fed00bc0323d62e9f859/eaa

## About the Role

* Experience deploying and scaling large language model inference infrastructure
 * Contributions to open-source ML frameworks or published research
 * Experience with distributed training on GPU clusters at scale
 * Background in feature stores and real-time inference systems
 * Track record of setting technical direction across multiple engineering teams

Nice to have

 * Experience deploying and scaling large language model inference infrastructure
 * Contributions to open-source ML frameworks or published research
 * Experience with distributed training on GPU clusters at scale
 * Background in feature stores and real-time inference systems
 * Track record of setting technical direction across multiple engineering teams

## Description

We are conducting a confidential search for a Principal Machine Learning Engineer to serve as the senior technical authority for ML systems within a technology organization based in Seattle, working hybrid. This is an individual-contributor role for an engineer who has shipped large-scale ML systems in production and wants to set technical direction without moving into people management.
In this role, you will design and build large-scale training and inference systems, own model architecture decisions for high-impact ML products, and drive best practices for experimentation, evaluation, and MLOps across the engineering organization. You will work closely with applied science and platform teams to take models from research prototype to production at scale, optimizing for latency, cost, and reliability.
You will mentor senior and staff engineers, set technical standards for model deployment, monitoring, and retraining pipelines, and represent ML engineering in architecture reviews. You will evaluate build-vs-buy decisions for ML infrastructure, stay current on the state of the art in deep learning and LLM systems, and translate emerging techniques into practical, production-grade improvements.


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