Summary

Posted: Oct 29, 2024

Role Number:200574288

Video is at the core of nearly all Apple products, and as an engineer on our team, you will research practical ML algorithms and build with us the next generation of video technology. We are looking for a highly ambitious individual, who will flourish working on technically meaningful problems at the frontier of deep learning and compression research. Your work will redefine the video experience for billions of users.

Description

In this role you will work together with colleagues to develop ML-based video approaches for current and future Apple products. This position requires a highly self-directed individual, who is comfortable working at the cutting edge of research. Strong and analytical skills will be critical towards solving challenging problems in uncharted technical territories. Your responsibilities will include: - Research various components of a DL-based approach to image/video processing problems: invent models, design appropriate datasets and pipelines to train them, prototype new architectures to improve performance, study existing literature, and so on. - Collaborate closely with team members to optimize the efficiency of the models and deploy them on particular hardware architectures. - Work with cross-functional teams to achieve quality and computational requirements towards shipping the technology. - Communicate: demonstrate the technology, present to leadership, discuss progress with colleagues, and so on.

• Master's degree in Computer Science, Electrical Engineering, or closely related fields.
• 5+ years of hands-on experience in ML research, which can include Ph.D. work.
• Proven track record of success in deep learning, with publications in top ML/CV venues.
• Familiarity with the latest ML and CV innovations.
• Software skills in common ML tools such as PyTorch or TensorFlow.
• Knowledge of ML and specifically deep learning: principles, model prototyping and architecture design, training procedures, visualization and debugging methodology, objective function design, and so on.

• Ph.D. in Computer Science, Electrical Engineering, or closely related fields.
• Experience implementing custom ops in CUDA.
• Experience in designing practical ML implementations and having ML experience in industry.
• Experience with generative modeling, optical flow, perceptual quality, ML for video or data compression.
• Excellent written and oral communication skills.

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 $143,100 and $264,200, 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.

• Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.