Machine Learning Engineer — Camera & Photos, Creative Foundations

Apple Apple · Big Tech · San Diego, CA · Machine Learning and AI

Machine Learning Engineer and Researcher to join the Creative Foundations team within Camera & Photos. This role involves inventing novel ML models at the intersection of research and product features, focusing on image understanding for consumer-facing applications. Responsibilities include designing architectures, training strategies, and intelligent systems, translating research into shippable features, and leveraging interpretability techniques. Requires MS/PhD, experience in ML/computer vision, proficiency in ML frameworks, and understanding of modern ML architectures. Preferred qualifications include a track record of creative problem-solving, published research, and specific computer vision experience.

What you'd actually do

  1. Pioneer novel approaches to image understanding — designing architectures, training strategies, and intelligent systems that push the boundaries of what our camera and photo experiences can do.
  2. Continuously survey state-of-the-art research, rapidly prototype high-potential ideas, and translate them into shippable features.
  3. Leverage model introspection and interpretability techniques to deeply understand why models behave the way they do and guide decisions accordingly.
  4. Collaborate across disciplines with product designers, software engineers, and aesthetic science researchers in an environment that values diverse perspectives, research rigor, and agility in an ever-evolving ML landscape.

Skills

Required

  • MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Electrical Engineering, Applied Mathematics, Statistics, or a related field — or equivalent practical experience demonstrating deep ML expertise.
  • Experience in machine learning, computer vision, or a related field (academic or industry), with a strong portfolio of building and shipping models or publishing research.
  • Deep understanding of modern ML architectures and techniques — including (but not limited to) transformers, diffusion models, contrastive learning, multi-modal models, and efficient neural network design and optimization.
  • Proficiency in ML frameworks such as PyTorch, and comfort working across the full model lifecycle from research exploration using large-scale data to production deployment.
  • Experience with image understanding tasks such as semantic segmentation, scene recognition, image captioning, visual question answering, image aesthetics, or image retrieval.
  • Strong fundamental software engineering background

Nice to have

  • A track record of creative problem-solving — taking an ambiguous challenge and finding an elegant, sometimes unconventional, ML-driven solution.
  • A genuine passion for pushing the boundaries of what's possible with machine learning and a deep curiosity for how intelligent systems can transform everyday experiences.
  • Published research at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, SIGGRAPH, etc.) is valued — but so is a strong portfolio of impactful shipped features or open-source contributions.
  • Comfort navigating ambiguity and working in a fast-moving R&D environment where the problem definition evolves alongside the solution.
  • A personal connection to photography or visual storytelling — whether through a creative practice, a deep appreciation for the craft, or simply an obsession with what makes a great image.
  • Specific computer vision experience in the areas of Semantic Image Understanding, Diffusion for Image Generation, Style Transfer, Computational Photography, Image Enhancement (Super-Resolution, Eenoising, etc.), Aesthetic Quality Assessment, Personalization (Few-Shot Adaptation)

What the JD emphasized

  • invent them
  • turning a theoretical breakthrough into a magical user experience
  • bridge the gap between what's possible in research and what's shippable in product
  • pioneer novel approaches
  • push the boundaries
  • translate them into shippable features
  • deeply understand why models behave the way they do
  • research rigor
  • building and shipping models or publishing research
  • full model lifecycle from research exploration using large-scale data to production deployment
  • published research at top-tier venues
  • impactful shipped features

Other signals

  • invent models
  • intersection of cutting-edge ML research and features
  • turning theoretical breakthrough into magical user experience
  • bridge the gap between research and shippable product
  • pioneer novel approaches to image understanding
  • designing architectures, training strategies, and intelligent systems
  • translate state-of-the-art advances into intelligent systems
  • leverage model introspection and interpretability techniques
  • collaborate across disciplines
  • research rigor
  • MS or PhD
  • building and shipping models or publishing research
  • transformers, diffusion models, contrastive learning, multi-modal models
  • full model lifecycle from research exploration to production deployment
  • image understanding tasks
  • creative problem-solving
  • published research at top-tier venues
  • impactful shipped features or open-source contributions
  • navigating ambiguity
  • fast-moving R&D environment
  • computational photography
  • image enhancement
  • aesthetic quality assessment
  • personalization