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Apple

Video Machine Learning Engineer, Audio & Media Technologies

19 days ago• San Diego, CA

About us

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

★ 3.7 out of 5 · 120 reviews

  • Size: 10,000+ employees
  • Industry: Technology: Manufacturing
View company profile ›

Why you should apply for a job to Apple:

  • Ranked as one of the Best Technology Companies in 2020
  • 3.7/5 in overall job satisfaction
  • 3.1/5 in supportive management
  • 66% say women are treated fairly and equally to men
  • 65% would recommend this company to other women
  • 66% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

Imagine what you could do here! At Apple, new insights have a way of becoming extraordinary products, services, and customer experiences very quickly. We are seeking a passionate and innovative machine learning engineer to join a team that is shaping the future of video intelligence. Our team develops cutting-edge machine learning technologies and systems that power intelligent, interactive video experiences across Apple's products - including Photos, Camera, Spotlight, FaceTime, AirPlay, and TV+. This is your opportunity to push the boundaries of what's possible in video understanding, processing, and delivery, and to see your work come to life in products used by hundreds of millions of people worldwide.

Description

As a Video Machine Learning Algorithm Engineer on our team, you will be at the forefront of designing, developing, and deploying machine learning solutions that redefine how video is experienced across Apple's ecosystem. You will collaborate with a world-class group of researchers and engineers to tackle challenging problems in video processing and understanding - from crafting novel neural network architectures to optimizing models for on-device performance. Your work will span the full lifecycle of innovation: researching state-of-the-art techniques, prototyping new ideas, training and evaluating models, and shipping production-quality solutions that delight users. If you thrive at the intersection of deep learning research and real-world product impact, this role will give you the platform to do the best work of your career.","responsibilities":"Design and develop novel machine learning algorithms and neural network architectures for video processing, compression, understanding, and enhancement.

Train, evaluate, and iterate on deep learning models using large-scale datasets to achieve state-of-the-art performance.

Optimize machine learning models for efficient deployment on Apple hardware, balancing quality, latency, and resource constraints.

Implement and integrate end-to-end machine learning pipelines into Apple's products and frameworks.

Collaborate cross-functionally with product, software, and hardware teams to define requirements and deliver cohesive solutions.

Investigate and prototype emerging research techniques to solve complex video-related challenges.

Analyze and benchmark model performance using rigorous metrics, driving continuous improvement in quality and efficiency.

Document technical designs, experimental results, and best practices to support team knowledge sharing and reproducibility.

Preferred Qualifications

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, with the ability to work effectively in cross-functional teams.

Minimum Qualifications

BS in Computer Science, Electrical Engineering, Machine Learning, or a related field

Strong foundation in deep learning and neural network design, including 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++, with demonstrated ability to debug and solve complex technical problems.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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Additional Information

  • Salary: $142,300 – $263,300
  • Posted: 19 days ago
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