What are the responsibilities and job description for the QA Engineer, AI position at Peloton?
QA Engineer, AI
New York, NY
The Role
The Artificial Intelligence team at Peloton is looking for a QA Engineer to drive QA efforts for our upcoming products. Their main focus will be to work closely with ML Engineers, data engineers, and data analysts to help support the future of machine learning and connected fitness. They will be responsible for building tools and infrastructure required for QA efforts. They will help staff and lead a team of contractors/vendors responsible for quality assurance for multi-platform, global, and rapidly growing products. They will also act as key point of contact for all QA aspects of releases, providing QA services and coordinating QA resources internally.
Responsibilities
- Establish and evolve formal QA processes, ensuring that the team is using industry-accepted best practices and adapt that specifically to our products.
- Oversee all aspects of quality assurance including establishing metrics and developing new tools and processes to ensure quality goals are met.
- Develop and execute test cases, scripts, plans and procedures (both manual and automated).
- Interface between product, data and internal system-wide QA teams in order to provide constant feedback on test results and discuss ways to improve.
- Lead a team of contractors/vendors responsible for quality assurance for multi-platform, global, and rapidly growing products.
- Mentor other QA members of the team.
Qualifications
- 4-6 Years of experience developing infrastructure and platforms to power QA efforts at scale.
- Strong programming background, with extensive experience in Python. Experience with C, C , Java, Swift, or more general purpose programming languages is a plus.
- Substantial experience with multiple technologies from the following list: AWS, Sagemaker, MLFlow, Airflow, TensorBoard, Anaconda, Jupyter, Kubernetes, MySQL, NoSQL, NFS, Spark.
- Entrepreneurial and self-directed, innovative, biased towards action in fast-paced environments.
- Able to take complete ownership of a feature or project.
Bonus Points
- Previous experience with developing machine learning QA infrastructure.
- Strong background working with large amounts of time series data and image data, associated annotations and meta-data.
- Experience setting up ML CI / CD pipelines, testing and validating code and components, testing and validating data, data schemas, and models.