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# Machine Learning Engineer, GenAI, Amazon Connect

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $168,100.0 - $227,400.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Engineering, Code Review, Nvidia CUDA, Computer Programming, Software Design Patterns, Distributed Systems, Machine Learning, Software Engineering, Alwayson, Amazon Connect, Graphics Processing Unit (GPU), Large Language Models, Information Technology, Optimization Algorithms, Build Process, Machine Learning Operations, Software Coding, Software Version Control, Programming Languages
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ed24bbfd1e684ae8

## About the Role

* 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
 * Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques, * Bachelor's degree in computer science or equivalent
 * 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
 * Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
 * Experience with CUDA kernels or ML/low-level kernels

## Description

Our machine learning engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You'll bring a passion for innovation, large language models, inference optimization, distributed systems, and cloud-native ML infrastructure. You'll also:

 * Design, develop, and research machine learning systems end-to-end - building robust ML solutions that translate data science prototypes into production-ready systems that drive real business outcomes.
 * Build, host, and maintain production-grade LLM serving and inference infrastructure - delivering high-quality, highly available, always-on AI systems that customers and internal teams can depend on.
 * Optimize the full inference stack for performance and cost-efficiency - applying techniques such as model quantization, batching strategies, KV-cache management, and accelerator tuning.
 * Partner with cross-functional teams and customers to deeply understand real-world challenges, and iteratively translate requirements into scalable, secure, and cost-effective machine learning solutions on AWS.


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