What are the responsibilities and job description for the Machine Learning Research Engineer position at Apple, Inc.?
The Human and Object Understanding team (HOUr) in the Intelligent System Experience (ISE) organization is looking for Research Engineers with a strong foundation in Machine Learning and Computer Vision to develop the next generation of multi-modal Human Sensing technologies. You will be part of a fast-paced, impact-driven Applied Research organization building foundation models for facial and full-body perception and working on cutting-edge machine learning that is at the heart of the most loved features on Apple platforms including Apple Intelligence, Camera, Photos, Visual Intelligence, etc.
Description
As a Machine Learning Research Engineer, you will be responsible for designing and developing cutting-edge AI/ML models for Human Sensing, with a focus on building robust cross-domain identity recognition systems. Multi-modal Human Sensing is a foundational capability that powers intelligent experiences based on key human traits such as identity, expression, clothing, action, gesture, gaze and human-object interaction. Major Apple Intelligence experiences such as personalized Natural Language Search, Memories Creation, as well as personalized Image Generation are powered by our ability to learn robust representations of visual human traits. Efficient real-time visual human sensing powers flagship Photography experiences such as Cinematic mode and Photographic Styles, communication experiences such as Center Stage, and paves the way for more natural human-device interactions, e.g., with the DockKit framework.
Minimum Qualifications
Master's or Ph.D. in Computer Science, Computer Engineering, or related fields, or equivalent professional experience in Computer Vision (CV) and Machine Learning (ML) research and development.
Proven track record of training, fine-tuning and evaluating deep learning models for vision tasks using modern ML architectures.
Background in research and innovation, demonstrated through publications in top-tier journals or conferences, patents, or impactful software developments.
Proficient in Python, PyTorch or equivalent deep learning frameworks.
Preferred Qualifications
Expert-level knowledge of state-of-the-art methods in face recognition or other facial analysis and biometric systems.
Hands-on experience training and scaling multi-modal large language models (LLMs) or large-scale vision-language models (VLMs)
Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines.
Description
As a Machine Learning Research Engineer, you will be responsible for designing and developing cutting-edge AI/ML models for Human Sensing, with a focus on building robust cross-domain identity recognition systems. Multi-modal Human Sensing is a foundational capability that powers intelligent experiences based on key human traits such as identity, expression, clothing, action, gesture, gaze and human-object interaction. Major Apple Intelligence experiences such as personalized Natural Language Search, Memories Creation, as well as personalized Image Generation are powered by our ability to learn robust representations of visual human traits. Efficient real-time visual human sensing powers flagship Photography experiences such as Cinematic mode and Photographic Styles, communication experiences such as Center Stage, and paves the way for more natural human-device interactions, e.g., with the DockKit framework.
Minimum Qualifications
Master's or Ph.D. in Computer Science, Computer Engineering, or related fields, or equivalent professional experience in Computer Vision (CV) and Machine Learning (ML) research and development.
Proven track record of training, fine-tuning and evaluating deep learning models for vision tasks using modern ML architectures.
Background in research and innovation, demonstrated through publications in top-tier journals or conferences, patents, or impactful software developments.
Proficient in Python, PyTorch or equivalent deep learning frameworks.
Preferred Qualifications
Expert-level knowledge of state-of-the-art methods in face recognition or other facial analysis and biometric systems.
Hands-on experience training and scaling multi-modal large language models (LLMs) or large-scale vision-language models (VLMs)
Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines.