Machine Learning Engineer, EXR OPS Tech Team
Amazon · Bellevue, Washington, USA ·
- Employment
- Full time
- Category
- ML ai
Are you passionate about building software that operates at massive scale and directly impacts millions of customers? As a Machine Learning Engineer at Amazon, In this role, you will help design, build, and deploy machine learning models and systems that power innovative solutions at scale. You will work closely with senior engineers and scientists to develop production-grade ML pipelines, contribute to model training and evaluation, and support the end-to-end ML lifecycle. This is an excellent opportunity for an early-career ML practitioner who is passionate about applying machine learning to solve real-world customer problems. You will design, build, and deliver features that solve real customer problems while raising the bar on code quality and system reliability. This is a role where your technical contributions matter, your ideas shape the direction of the software you own, and your mentorship helps those around you grow. If you are a curious, collaborative engineer who thrives on turning ambiguous requirements into clean, maintainable solutions, we want to hear from you.
Key job responsibilities
- Design, develop, and maintain ML data pipelines and feature engineering workflows
- Design and implement software solutions across major components of your team's systems, balancing functional requirements with scalability, performance, and security considerations.
- Collaborate with Applied Scientists and Data Scientists to translate research prototypes into production systems
- Monitor model performance in production and implement retraining strategies
- Contribute to the full software development lifecycle including product definition, scoping, design, code review, testing, deployment, and operational support, while coaching teammates on producing high-quality, maintainable code.
- Identify root causes of operational issues and drive permanent fixes, continuously improving your team's software reliability and operational processes.
- Collaborate with customers, stakeholders, and cross-functional peers to understand business context and ensure your solutions deliver the right customer value.
- Evaluate new technical approaches and tools, including generative artificial intelligence capabilities, and recommend adoption where they improve productivity and solution quality.
A day in the life
You might start your morning reviewing a design document for a feature you are leading, then jump into a code review where you provide constructive feedback to a teammate. After a quick sync with your product partners to clarify requirements, you spend focused time writing and testing code. In the afternoon, you investigate an operational metric that looks off, trace it to a root cause, and begin implementing a fix. Throughout, you are mentoring newer engineers and contributing to decisions that shape how your team's software evolves.
Key job responsibilities
- Design, develop, and maintain ML data pipelines and feature engineering workflows
- Design and implement software solutions across major components of your team's systems, balancing functional requirements with scalability, performance, and security considerations.
- Collaborate with Applied Scientists and Data Scientists to translate research prototypes into production systems
- Monitor model performance in production and implement retraining strategies
- Contribute to the full software development lifecycle including product definition, scoping, design, code review, testing, deployment, and operational support, while coaching teammates on producing high-quality, maintainable code.
- Identify root causes of operational issues and drive permanent fixes, continuously improving your team's software reliability and operational processes.
- Collaborate with customers, stakeholders, and cross-functional peers to understand business context and ensure your solutions deliver the right customer value.
- Evaluate new technical approaches and tools, including generative artificial intelligence capabilities, and recommend adoption where they improve productivity and solution quality.
A day in the life
You might start your morning reviewing a design document for a feature you are leading, then jump into a code review where you provide constructive feedback to a teammate. After a quick sync with your product partners to clarify requirements, you spend focused time writing and testing code. In the afternoon, you investigate an operational metric that looks off, trace it to a root cause, and begin implementing a fix. Throughout, you are mentoring newer engineers and contributing to decisions that shape how your team's software evolves.