Camera Software - Sr. Machine Learning Engineer

Apple Apple · Big Tech · Cupertino, CA +1 · Software and Services

Senior ML Engineer at Apple's Camera Intelligence team, focusing on building and shipping on-device ML models for iPhone camera features. Requires expertise in computer vision, deep learning, and productizing ML research from prototype to efficient inference at the edge. Role involves technical leadership and collaboration.

What you'd actually do

  1. You will apply your ML and computer vision expertise to build, evaluate, and ship models that power Apple's imaging experiences, turning early-stage ideas into commercially viable, efficient, on-device algorithms.
  2. You'll stay current with developments across computer vision, deep learning, and adjacent ML fields (including LLMs and multimodal models where relevant), and use that knowledge to influence technical direction across the team and Apple's products.
  3. Demonstrated technical leadership: setting technical direction, driving cross-functional initiatives, and mentoring other engineers/researchers.
  4. Depth in one or more of: image processing, optical flow, object tracking/registration, generative models, or modern deep learning architectures.
  5. Experience with NLP, LLMs, or vision-language models is a strong plus.

Skills

Required

  • 10+ years of combined research and hands-on engineering experience building and shipping ML systems
  • strong foundation in computer vision
  • productizing ML research
  • taking models from prototype to on-device deployment
  • optimization
  • quantization
  • efficient inference at the edge
  • designing and scaling ML pipelines
  • large, real-world datasets
  • technical leadership
  • setting technical direction
  • driving cross-functional initiatives
  • mentoring other engineers/researchers
  • image processing
  • optical flow
  • object tracking/registration
  • generative models
  • modern deep learning architectures
  • strong software engineering fundamentals
  • programming
  • debugging
  • system design
  • excellent problem-solving
  • critical thinking
  • cross-functional communication skills

Nice to have

  • PyTorch
  • NumPy
  • Pandas
  • OpenCV
  • Pillow
  • NLP
  • LLMs
  • vision-language models
  • optimizing models for on-device/edge inference
  • quantization
  • pruning
  • distillation
  • deployment on accelerators such as the Apple Neural Engine
  • C++
  • Objective-C

What the JD emphasized

  • productizing ML research
  • on-device deployment
  • efficient inference at the edge
  • large, real-world datasets
  • technical leadership
  • modern deep learning architectures

Other signals

  • shipping ML models
  • on-device deployment
  • production ML systems