Senior Software Engineer, Siri AI Data & Privacy Quality Engineering
Apple
On-site
Cupertino, California, United States
We are seeking a Senior Software Engineer to join our Siri AI Data and Privacy Quality Engineering team. In this role, you will be responsible for designing, building, and evolving the evaluation environments and fundamental assertions to validate the instrumentation of our AI assistant products at scale with a focus on user privacy. You will create tools and frameworks for instrumentation and privacy evaluation, ensuring that our AI products meet their privacy promises and are instrumented for trustworthy measurement. Your work will involve collaborating closely with data and product engineering teams to provide evaluation methodologies and automation frameworks within a micro-services architecture.
This position is ideal for systems & framework engineers who are passionate about quality, enjoy creating scalable evaluation systems, and are eager to contribute to the success of pioneering AI products. If you feel this is you, we'd love to hear from you!
- 5+ years of proven experience designing, implementing, and optimizing large-scale data-driven platforms and frameworks, APIs, services, and tools
- Thorough understanding of backend architecture, privacy-preserving data practices, and large-scale system design
- Strong programming skills in Swift, with Python experience being highly valued
- Experience in designing and building scalable ETL pipelines, high-performance data stores, and automated workflows
- Experience building dashboards and analytics solutions using tools like Tableau, Grafana, Superset, or Splunk to visualize KPIs and monitor data quality
- Demonstrated success in collaborating cross-functionally with engineering, machine learning, and data science teams to solve sophisticated challenges
- BS/MS or equivalent experience in Computer Science, Engineering, or a related field
- Deep understating about large scale data validation platforms with focus on privacy
- Experience building and deploying applications with Kubernetes
- Knowledge of statistics-based evaluation approaches, ML training pipelines, and techniques for enhancing the accuracy of ML systems
- Strong attention to detail and proven track record of delving into data, uncovering hidden patterns, and conducting comprehensive error/deviation analysis