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Machine Learning Engineer — Camera & Photos, Creative Foundations
Apple
H1B ✓On-sitesenior levelPosted April 23, 2026
About the Role
You will pioneer novel approaches to image understanding by designing architectures and training strategies for camera and photo experiences. You will also translate state-of-the-art research into shippable product features while collaborating with cross-functional teams.
Requirements
Candidates must hold an MS or PhD in a relevant technical field or possess equivalent practical experience demonstrating deep ML expertise. A strong portfolio of building and shipping models or published research is required, along with proficiency in ML frameworks like PyTorch.
Full Job Description
We're looking for a Machine Learning Engineer and Researcher to join the Creative Foundations team within Camera & Photos. In this role, you won't just implement models — you'll invent them. You'll work at the intersection of cutting-edge ML research and the features that hundreds of millions of people use every day to capture, relive, and share their most meaningful moments.
This is a role for someone who gets excited about turning a theoretical breakthrough into a magical user experience. You'll bridge the gap between what's possible in research and what's shippable in product — translating state-of-the-art advances in image understanding into intelligent systems that feel intuitive and delightful. We're especially drawn to those with a passion for photography and experience with the visual and creative domains that make this work so meaningful.
As a Machine Learning Engineer on the Creative Foundations team, you will 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. You'll continuously survey state-of-the-art research, rapidly prototype high-potential ideas, and translate them into shippable features — while also leveraging model introspection and interpretability techniques to deeply understand why models behave the way they do and guide decisions accordingly. You'll 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.
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
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)
Description
As a Machine Learning Engineer on the Creative Foundations team, you will 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. You'll continuously survey state-of-the-art research, rapidly prototype high-potential ideas, and translate them into shippable features — while also leveraging model introspection and interpretability techniques to deeply understand why models behave the way they do and guide decisions accordingly. You'll 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.
Minimum Qualifications
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
Preferred Qualifications
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)