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# Principal Machine Learning Engineer, Conversational AI Modeling and Learning

- **Company:** Amazon.com, Inc.
- **Location:** Bellevue, WA, United States
- **Salary:** $200,100.0 - $270,600.0
- **Contract:** Internship / Graduate position
- **Skills:** Amazon Alexa, Data Structures, Software Debugging, Distributed Computing Environment, Distributed Systems, Machine Learning, Object-Oriented Software Development, Service-Oriented Architecture, Software Engineering, Reinforcement Learning, Chatbots, Large Language Models
- **Published:** September 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5782cab433c45659

## About the Role

12+ years of non-internship professional software development experience
Knowledge of object-oriented design, data structures, and algorithms, Experience designing and building large-scale systems in a multi-tiered, distributed environment (Service Oriented Architecture)

## Description

Define and drive the engineering roadmap and architecture for the agentic AI platform: evaluation, training, self-learning, and serving for LLM-based agents in production

Architect large-scale agentic evaluation infrastructure: isolated sandboxed execution, recreatable environments, verifiable scoring, and reproducibility at hundreds of concurrent trials, so model decisions rest on trustworthy numbers

Build and scale RL and post-training systems for agentic workloads: 256K+ token contexts, multi-turn trajectory training, train/inference engine consistency, and reward attribution across long sessions

Design the serving and inference architecture for agentic traffic (long sessions, output-generation-bound workloads, KV-cache-centric optimization), co-designing with inference-infrastructure partner teams

Set the technical bar across the organization: raise engineering standards through design reviews, operational excellence, and deep dives on the hardest cross-system problems

Translate ambiguous product and science requirements into platform interfaces partner teams can build on; influence senior leadership on build-vs-adopt and ownership decisions

Mentor and grow senior and principal-track engineers across multiple teams

A day in the life
You might spend the morning in a design review for the next generation of the evaluation platform's execution layer, midday debugging why a 100K-token training session diverges between the rollout engine and the learner, and the afternoon with the serving team deciding which KV-cache optimizations justify architectural investment before a product launch. You work daily with applied scientists and other engineers, and your systems are the reason their results are trustworthy and shippable.

About the team
Our organization owns the applied science and platform engineering for Alexa's agentic experiences. We operate at the intersection of large language models, reinforcement learning with verifiable rewards, agentic architectures, and large-scale distributed systems, serving customers across dozens of languages and device types. Our platform provides the shared evaluation, training, self-learning, and serving foundation for Alexa's flagship agent programs and the broader agent portfolio behind them.