Computer Vision & Machine Learning Engineer

Role Number: 200667649-3956

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build or service we create is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something — you'll add something!

We are a team of computer vision and machine learning engineers building real-time perception systems, motion synthesis, and human understanding technologies for current and future Apple products. The VCV org is a centralized applied research and engineering organization responsible for developing real-time on-device Computer Vision and Machine Perception technologies across Apple products. We are looking for engineers with expertise in deep learning-focused computer vision for human understanding, motion synthesis, visual recognition, biometric algorithms, and 3D perception systems.

In this role, you will help design, build, and ship core perception technologies, motion synthesis systems, and human understanding algorithms used by millions of users across Apple's ecosystem.

Description

You will work on cutting-edge computer vision and machine learning problems, developing algorithms and systems that enable natural human-computer interaction. This includes human perception, motion synthesis, biometric recognition, 3D vision, and performance-critical real-time systems.

You will be responsible for developing and optimizing computer vision and machine learning algorithms for human understanding, including pose estimation, gesture recognition, facial analysis, and behavioral modeling. You will build motion synthesis systems and algorithms for realistic human motion generation and animation, design and implement biometric algorithms for secure authentication and identification systems, and create real-time 3D perception and tracking systems for spatial computing and AR/VR applications. As a member of a fast-paced team, you have the unique and rewarding opportunity to shape upcoming products that will delight and inspire millions of people every day.

Minimum Qualifications

• Master's or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field

• Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g. vision, language, video, etc.)

• Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX

• Experience with rapid prototyping, reproduction, and validation of research ideas

• Strong mathematical foundations in machine learning, computer vision, or related fields

• Experience with foundation model architectures and training methodologies

• Experience working effectively in a multi-functional, collaborative environment

Preferred Qualifications

• PhD, or equivalent practical experience, in Computer Science, Machine Learning, Computer Vision, or a related technical field

• Demonstrated expertise in deep learning, with either: A publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, COLM, etc), or a strong track record of applying deep learning techniques to real-world products

• Experience with foundation models (language or multimodal) including training, fine-tuning, and deployment

• Experience applying foundation models to build autonomous or semi-autonomous agents, including planning, task decomposition, and multi-step reasoning

• Experience with multimodal pretraining, vision-language models, video-language models, and multimodal alignment

• Experience with large-scale distributed training and model parallelism

• Strong communication skills and ability to present research findings to both technical and non-technical audiences