AIML - Sr Backend Software Engineer, Data and ML Innovation

Position: Analytics Engineer – Siri Data Organization
Location: Cupertino CA - Hybrid (Onsite Tuesday–Thursday)
Duration: 3+ months - Possibility of Extension
Interview: Video

Would you like to play a part in the next revolution in human-computer interaction? Join the team behind Siri—the intelligent assistant used by millions to get things done with a simple voice command. The Siri Data Organization uses data as the voice of the customer, and the Analytics team drives the evolution of Siri through measurement and analysis of the user experience.

As an Analytics Engineer, you will build scalable data products and tools that empower analytics, engineering, and product teams to improve the Siri experience across Apple devices.

• Experience engineering metrics and statistical insights from massive, complex datasets (Hive, Spark MLlib, Druid, Solr, Kafka).
• Proficiency in at least one programming language such as Python or Scala, with experience working in a team development environment (Git, testing, code reviews).
• Proven ability to build robust data and analytics pipelines, with a strong sense for automation (Oozie, Airflow).
• Self-driven, curious, and creative critical thinker with a strong desire to improve systems and processes.
• Comfortable working in ambiguous environments; able to anticipate needs and synthesize insights.
• Strong communication skills with the ability to influence decisions through clear verbal and written communication.

• Work closely with Siri Data and Siri Engineering teams to build information products from high-volume, complex logging generated by Siri computational processes.
• Become an expert in Siri's architecture and data sources; contribute to shaping data instrumentation across Siri functionalities.
• Build data products, dashboards, visualizations, and analytical tools with an emphasis on automation and scalability.
• Develop microservices to enable programmatic integration of analytics across the Siri organization.
• Create metadata frameworks and tools that improve transparency, reproducibility, and governance of analytics processes.

• B.S., M.S., or Ph.D. in:
  Computer Science, Computer Engineering, Statistics, Bioinformatics, Applied Mathematics, Physics, or related field — or equivalent practical experience.