This is a role in the AWS Inferentia cloud-scale machine learning accelerator team. Would you like to test the newest forms of compute? How often have you had an opportunity to be a founding member of a team that is tasked with solving a huge business problem through innovative technology? Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains, so a background in web-based distributed systems, Linux applications and server based platforms is valuable.

As a Senior QA Engineer, you will help lead and drive the team that will test a new generation of ML hardware/software platform for AWS. You will define test strategies, quality metrics and automation coverage that will drive the QA team charter. You should be an expert with testing methodologies. We would prefer you to have experience working on platforms with both hardware and software elements and with testing from end to end.

You will help tackle a variety of customer challenges. Given that this is an early-stage initiative, you will play an active role in translating business and functional requirements into concrete deliverables and test ML systems in partnership with other technology leaders within the team.

Key job responsibilities
- Design and develop comprehensive test strategies and QA methodologies to ensure the quality and reliability of AWS Neuron
- Collaborate and influence cross-functional teams including developers, product managers, and solution architects to understand ML quality requirements and design effective test strategies.
- Define release planning and provide input on the readiness of AWS Neuron SDK for production deployment.
- Lead and mentor a team of QA engineers, providing guidance, technical expertise, and ensuring adherence to best practices.
- Create, maintain, and execute test cases, test scripts, and test scenarios for various software components, including functional, regression, performance, and security testing.
- Identify and document defects, issues, and potential areas of improvement in the software development process.
- Perform root cause analysis of complex issues and work with the development team to resolve issues and enhance the overall product quality.
- Stay up-to-date with ML industry trends, ML technologies, and best practices in QA and ML testing, and apply this knowledge to continuously improve testing processes.
- Collaborate with DevOps engineers to implement and maintain automated testing frameworks and test suites.
- Define key metrics for measuring and reporting on the quality of software products and test effectiveness.