Principal ML Software Engineer, Creativity Apps

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Principal ML Software Engineer for Apple's Creativity Apps team, focusing on building and optimizing on-device ML features for creative editing tools. The role involves architecting efficient execution capabilities, implementing ML algorithms on-device, and collaborating with scientists and designers to deliver performant, ML-based application features, with a strong emphasis on computer vision and iOS platform capabilities.

What you'd actually do

  1. Work closely in a diverse team including scientists, engineers, and designers to deliver performant, ML-based application features in the area of Computer Vision
  2. Architect and build efficient on-device execution capabilities for ML-heavy workloads, taking advantage of iOS platform capabilities and underlying hardware architecture
  3. Collaborate with ML scientists and engineers to implement machine learning algorithms on-device, ensuring parity between on-device inference and training-time evaluation
  4. Analyze and optimize the performance of machine learning models and systems, working backwards from a deep understanding of product needs.
  5. Formulate concrete requirements from broad feature definitions, drive consensus and scope the required effort for planning

Skills

Required

  • BS/MS in Computer Science or related field
  • 10+ years of significant iOS development experience
  • Strong track record in building both, user-facing apps and system-level components, driving significant product impact
  • Strong technical judgment and communication skills
  • Deep knowledge of the iOS ecosystem
  • Experience optimizing models and algorithms to run efficiently on resource constrained platforms
  • Excellent understanding of an ML-based product lifecycle
  • Demonstrated ability to lead and set direction for ML integration across organizational boundaries
  • Experience as a technical lead
  • Swift
  • Objective-C
  • CoreML

Nice to have

  • PhD in Computer Science, Machine Learning or related field
  • Solid understanding of computer vision, machine learning and deep learning techniques
  • Vision Transformers
  • Multimodal LLMs
  • Diffusion models
  • modern camera ISP and digital image processing algorithms and models
  • photography

What the JD emphasized

  • on-device execution
  • on-device inference
  • resource constrained platforms
  • ML-based application features
  • Computer Vision

Other signals

  • on-device ML execution
  • ML-based application features
  • optimize ML models and systems