About the position
APPLE INC has the following available in Austin, Texas. Analyze software design by participating in product and design reviews, reviewing engineering requirement documents to derive quality needs, design, develop and run automated and manual software tests, and provide wider quality considerations for software, such as regarding process, dependencies and timelines. Build and maintain Automation frameworks to qualify software changes for machine learning pipelines. Develop tools to aid Continuous Integration and Delivery by stabilizing automation, automating triggering validations post deployments and for promotion between environments. Collaborate with cross-functional teams to understand product requirements, design test strategy and dive deep to understand and test machine-learning algorithms and pipelines to deliver revenue critical systems to production with high quality. Debug production issues and integrate with monitoring infrastructures to help ascertain the system health of Machine learning pipelines. Build data quality dashboards for observing trends in model performance to catch anomalies due to variations in data observed with live traffic. Collaborate with internal and external Quality Engineering teams on tentpole initiatives to perform integration testing to validate quality of contracts and data flow between different systems. Work on resiliency testing of platform components at scale on hybrid cloud environment to help find issues that would cause instability during various failure modes. 40 hours/week.
Responsibilities
- Analyze software design by participating in product and design reviews.
- Review engineering requirement documents to derive quality needs.
- Design, develop and run automated and manual software tests.
- Provide wider quality considerations for software, such as regarding process, dependencies and timelines.
- Build and maintain Automation frameworks to qualify software changes for machine learning pipelines.
- Develop tools to aid Continuous Integration and Delivery by stabilizing automation.
- Automate triggering validations post deployments and for promotion between environments.
- Collaborate with cross-functional teams to understand product requirements.
- Design test strategy and dive deep to understand and test machine-learning algorithms and pipelines.
- Debug production issues and integrate with monitoring infrastructures.
- Build data quality dashboards for observing trends in model performance.
- Collaborate with internal and external Quality Engineering teams on tentpole initiatives.
- Perform integration testing to validate quality of contracts and data flow between different systems.
- Work on resiliency testing of platform components at scale on hybrid cloud environment.
Requirements
- Bachelor's degree or foreign equivalent in Software Engineering, Computer Science or related field.
- Creating software test plans and writing tests for software validation.
- Using Java to write software automation and test cases.
- Using SQL to write queries to validate data quality.
- Performing object-oriented code engineering for testing and maintaining software.
- Performing data and big data engineering to process and validate data at scale.
- Utilizing software development and version control tools such as Git to store and manage code.
- Using mathematical principles like probability, advanced arithmetic and integral calculus to improve the quality processes of models.
- Data collection and analysis methods: writing code to query hive tables, plotting trend graphs and analyzing for anomalies.
- Building queries to join data across various datasets to check for functional correctness.
