Apple is looking to solve the problem of ensuring user privacy and trustworthy measurement in their AI assistant products, specifically Siri, by designing, building, and evolving evaluation environments and fundamental assertions for instrumentation at scale.
Requirements
- 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
- Deep understating about large scale data validation platforms with focus on privacy
- Experience building and deploying applications with Kubernetes
Responsibilities
- 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.
- Create tools and frameworks for instrumentation and privacy evaluation, ensuring that our AI products meet their privacy promises and are instrumented for trustworthy measurement.
- Provide evaluation methodologies and automation frameworks within a micro-services architecture.
- Designing, implementing, and optimizing large-scale data-driven platforms and frameworks, APIs, services, and tools
- Designing and building scalable ETL pipelines, high-performance data stores, and automated workflows
- Building dashboards and analytics solutions using tools like Tableau, Grafana, Superset, or Splunk to visualize KPIs and monitor data quality
- Delving into data, uncovering hidden patterns, and conducting comprehensive error/deviation analysis
Other
- Demonstrated success in collaborating cross-functionally with engineering, machine learning, and data science teams to solve sophisticated challenges
- Strong attention to detail
- BS/MS or equivalent experience in Computer Science, Engineering, or a related field
- Knowledge of statistics-based evaluation approaches, ML training pipelines, and techniques for enhancing the accuracy of ML systems
- Individual imaginations gather together, committing to the values that lead to phenomenal work.
