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Apple Machine Learning Engineer United States, California, Cupertino 326637967
18.11.2024
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Description
Your responsibilities include, but not limited to: - Work on data collection and curation for training/testing/validation of machine learning models. - Investigate the latest deep learning based low-level vision methods. - Harness the power of generative and multi-modal foundation models in improving the quality of video features across Apple products.
Minimum Qualifications
- Masters degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields.
- Knowledge of the principles, algorithms, and techniques used in machine learning and computer vision with first-hand experiences.
- Programming skills and hands-on experience with Python and deep learning frameworks such as PyTorch.
- Hands on experience training large generative neural networks (GAN, Diffusion Models).
Preferred Qualifications
- PhD degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields.
- Knowledge of low-level vision algorithms such as spatial and temporal image/video scaling, noise reduction, etc.
- Background in digital signal and image processing
- Hands on experience with multi-modal foundation models
- Publication record in top-tier conferences (e.g., CVPR, ICCV, SIGGRAPH, ECCV, NeurIPS, ICML, ICLR).
- Excellent independent problem-solving skills
- Excellent written and oral communication skills
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 $143,100 and $264,200, and your base pay will depend on your skills, qualifications, experience, and location.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.
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