Visual Intelligence and Machine Learning Research Scientist

San Jose, CA Full-Time 2026-06-30

About Apple

Apple is a global technology company known for designing, manufacturing, and marketing consumer electronics, computer software, and online services. Founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976, the company is headquartered in Cupertino, California. Apple is recognized for its innovative products like the iPhone, iPad, Mac, Apple Watch, and Apple TV, as well as its software ecosystems including iOS, macOS, watchOS, and various services. The company's commitment to cutting-edge research and development aims to create user-centric experiences.

About the Position

Introduction

Apple is a place where individual imaginations come together to create great work. Every product and service we deliver is the result of collaborative efforts to strengthen each other's ideas. You won't just join something; you'll contribute to it.

Our team operates at the intersection of computational neuroscience, visual intelligence, and AI, translating cutting-edge research into tangible user experiences. We prototype algorithms from initial concepts to shipping features, collaborating closely with app teams and designers to ensure our science profoundly impacts how people interact with Apple products. If you aspire for your research to have an impact beyond academic papers, this is the environment where it happens.

Responsibilities

We are seeking a Visual Intelligence and Machine Learning Scientist who adeptly bridges computational neuroscience and modern AI. You will be responsible for building models that interpret human visual attention, behavioral states, comfort, and perceptual salience from video and multimodal sensor streams. This role requires a history of original research contributions, a drive to implement algorithms with real-world data, and the flexibility to guide an idea from exploratory research through to a polished user experience.

Requirements

MS with 3+ years of research experience in Computer Science, Biomedical Engineering, Computational Neuroscience, Applied Mathematics, or a related field, with a focus on computer vision or machine learning.
Demonstrated research contributions to visual intelligence or perceptual modeling, including peer-reviewed publications or equivalent industry impact.
Deep expertise in visual perception: saliency modeling, object detection and localization, optical flow, depth estimation, and multimodal learning including sensor fusion with IMU and temporal signals.
Strong hands-on experience training and deploying deep learning models using modern frameworks (PyTorch, TensorFlow, JAX) and Python scientific libraries (NumPy, OpenCV, scikit-learn).
Strong mathematical foundations in linear algebra, probability, optimization, and signal processing, with experience in large-scale dataset curation and evaluation methodology.
Excellent communication and collaboration skills; ability to thrive in a fast-paced environment alongside scientists, engineers, designers, and domain experts from the behavioral and cognitive sciences.

Nice to Have

PhD in Computer Science, Biomedical Engineering, Computational Neuroscience, Applied Mathematics, or a related field, with a focus on computer vision or machine learning.
Experience with vision foundation models, vision transformers (ViT), and adapter-based fine-tuning for perceptual or behavioral downstream tasks.
Familiarity with generative AI and neural rendering approaches (diffusion models, implicit neural representations) as components of computational perception pipelines.
Background in computational modeling of perceptual or behavioral phenomena: modeling user state, comfort, visual salience, or behavioral response from video or sensor data.
Experience developing or deploying models for spatial computing platforms (AR/VR headsets, iOS, macOS).
Track record of shipping algorithms in resource-constrained, real-time on-device inference environments.
Ability to be versatile across a multi-faceted role, moving fluidly between deep research, engineering rigor, and direct collaboration with product and design teams to bring science to life in user-facing features.