Machine Learning Engineer — Camera & Photos, Creative Foundations

Were looking for a Machine Learning Engineer and Researcher to join the Creative Foundations team within Camera u0026 this role you wont just implement models youll invent them. Youll 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 is a role for someone who gets excited about turning a theoretical breakthrough into a magical user experience. Youll bridge the gap between whats possible in research and whats shippable in product translating state-of-the-art advances in image understanding into intelligent systems that feel intuitive and delightful. Were especially drawn to those with a passion for photography and experience with the visual and creative domains that make this work so meaningful.n

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. Youll 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. Youll 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 in machine learning computer vision or a related field (academic or industry) with a strong portfolio of building and shipping models or publishing 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 in ML frameworks such as PyTorch and comfort working across the full model lifecycle from research exploration using large-scale data to production with image understanding tasks such as semantic segmentation scene recognition image captioning visual question answering image aesthetics or image fundamental software engineering background

A track record of creative problem-solving taking an ambiguous challenge and finding an elegant sometimes unconventional ML-driven genuine passion for pushing the boundaries of whats possible with machine learning and a deep curiosity for how intelligent systems can transform everyday 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 navigating ambiguity and working in a fast-moving Ru0026D environment where the problem definition evolves alongside the 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 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)