Senior Applied ML Researcher - Video Apps

Summary

We are seeking a Senior Applied ML Researcher to design, train, and deploy state-of-the-art models for visual and audio understanding. You will work on challenging problems at the intersection of computer vision, audio signal processing, and multimodal learning, enabling intelligent systems that can see, hear, and reason about the world.

You will collaborate closely with research scientists, engineers, and product teams to find novel applications of Deep Machine Learning capabilities to assist our creative user base. Your mission is to elevate the workflows of millions of creators by combining generative AI with Apple’s human-centered design principles.

Description

Design and train deep neural networks for video, image, audio, and audio-visual tasks.
Build models for audio-visual representation learning, cross-modal alignment, and fusion.
Develop solutions for tasks such as:
Video understanding and temporal modeling.
Audio-visual event detection.
Speech, sound, and scene understanding.
Multimodal classification, detection, and localization.

Preferred Qualifications

PhD in computer science, machine learning, or a related field, or equivalent practical experience.
Publications in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, etc.)
Experience with self-supervised or foundation model pre-training
Open-source contributions in vision, audio, or multimodal AI
Bonus: Experience with Objective-C and/or Swift for on-device deployment

Minimum Qualifications

MS in Computer Science, Machine Learning, or a related field, or equivalent practical experience
4+ years of experience in deep learning or machine learning engineering
strong expertise in deep neural networks and modern training workflows
8 years + Hands-on experience with computer vision and/or audio modeling
Proficiency in Python and deep learning frameworks (PyTorch preferred)
Solid understanding of linear algebra, probability, and optimization
Ability to build intuition from problem statement and translate to dataset requirement, neural network design and loss functions