ISE, SIML - Data Acquisition Manager

Santa Clara Valley (Cupertino), California, United States
Machine Learning and AI


Weekly Hours: 40
Role Number:200240136
Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people capture, discover and share the most special moments of their lives? We truly believe it can! The System Intelligence and Machine Learning (SIML) group is responsible for crafting machine learning solutions to extract high level structure information from images, videos and text shipping on all Apple platforms (macOS, iOS, tvOS, watchOS). Examples include face recognition, scene classification, OCR, handwriting recognition as well as the support for internal tools. The group combines research and development in a dynamic and engaging environment. Our data team is responsible for designing and building high quality datasets at scale. At the heart of machine learning, data defines how Apple features and products operate and what is the final user experience that will impact millions of our customers. This is an exciting time to join us: grow fast, and have an impact on multiple key features on your first day at Apple!

Key Qualifications

  • Demonstrated capacity to lead a team, with a focus on collective success and product impact
  • Excellence in relationship building across organizations and functions (R&D, privacy and legal, infrastructure...)
  • Strong experience in leading operations and in establishing agile processes that guarantee a high level of service in a context of growing and evolving data needs (assets + metadata)
  • Passion to create great products and to understand the challenges associated to building datasets for machine learning features, targeting a global and diverse user base, while addressing the challenges of inclusion, bias removal, fairness, etc.
  • Ability to consistently innovate with internal/external partnerships and with acquisition methods that maximize value along the ML chain (eg time to delivery, quality and diversity of assets, work required to label/consume assets)
  • Excellent communication and interpersonal skills


Our team focuses on data acquisition, data science, annotation, and robustness analysis. Each year, we power dozens of features and work closely with ML teams across the entire company. Apple's commitment to deliver incredible experiences to a global and diverse set of users in full respect of their privacy leads our team to explore innovative data collection processes. This position focuses on managing and growing a team dedicated to the generation/ acquisition of high quality assets and metadata to support R&D efforts in the software organization. This requires to: • Drive acquisition operations from existing and new sources (internal/external studies, licensing...) to serve ML needs across various domains (visual, text, audio), with a focus on reducing lead time and workload, and increasing diversity/throughput/coverage • Establish processes that facilitate the expression of needs and the efficient planning/ tracking of data acquisition efforts • Spearhead smart collection efforts (and/or data synthesis work) that minimize (or remove) the complexity of ulterior annotations • Beyond assets, develop innovative solutions to collect metadata (eg feedback, actions) and lead user studies in order to guide R&D work, in a way compatible with Apple values • Become org champion on the subject of fairness and potential biases, pushing all parties and implementing processes to keep diversity and global coverage of Apple users in mind when analyzing current datasets and creating new ones • Lead and develop relationship with support teams (privacy, legal, procurement, security), internal partners (eg USG, AIML) and external parties • Promote reciprocal access and sharing of data across the company • Design and pilot the implementation of tools to facilitate the contributions of internal volunteers

Education & Experience

Bachelors degree in: Marketing, Legal, Business Administration, Computer Science, Mathematics, Physics, or equivalent experience

Additional Requirements