Video Machine Learning Engineer, Audio & Media Technologies

Apple Apple · Big Tech · San Diego, CA · Software and Services

Video Machine Learning Engineer at Apple, focusing on designing, developing, and deploying ML solutions for video processing, understanding, and enhancement. The role involves researching, prototyping, training, evaluating, and optimizing models for on-device deployment across various Apple products. It bridges deep learning research with real-world product impact.

What you'd actually do

  1. Design and develop novel machine learning algorithms and neural network architectures for video processing, compression, understanding, and enhancement.
  2. Train, evaluate, and iterate on deep learning models using large-scale datasets to achieve state-of-the-art performance.
  3. Optimize machine learning models for efficient deployment on Apple hardware, balancing quality, latency, and resource constraints.
  4. Implement and integrate end-to-end machine learning pipelines into Apple's products and frameworks.
  5. Collaborate cross-functionally with product, software, and hardware teams to define requirements and deliver cohesive solutions.

Skills

Required

  • BS in Computer Science, Electrical Engineering, Machine Learning, or a related field
  • Strong foundation in deep learning and neural network design
  • hands-on experience building, training, and deploying models
  • Proficiency in one or more deep learning frameworks such as PyTorch or TensorFlow
  • Experience with computer vision and/or image and video processing techniques
  • Strong programming skills in Python and/or C/C++
  • demonstrated ability to debug and solve complex technical problems

Nice to have

  • MS or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field with a focus on video or visual computing
  • Experience with video codec and compression techniques, including familiarity with standards such as H.264, H.265/HEVC, AV1, or VVC
  • Knowledge of ML-based image and video codecs, including neural compression and learned representations
  • Experience with diffusion models and generative approaches for image and video synthesis or enhancement
  • Familiarity with advanced video quality metrics (e.g., VMAF, LPIPS, DISTS) and perceptual quality evaluation methodologies
  • Experience optimizing and deploying neural networks on edge devices or mobile platforms
  • Published research in top-tier venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML) in relevant areas
  • Strong communication and collaboration skills
  • ability to work effectively in cross-functional teams

What the JD emphasized

  • shipping production-quality solutions
  • on-device performance
  • state-of-the-art techniques
  • training and evaluating models

Other signals

  • shipping production-quality solutions
  • optimize models for on-device performance
  • researching state-of-the-art techniques
  • training and evaluating models