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Apple ML Data Scientist - Computer Vision United States, California, Sunnyvale 491799937
25.07.2024
Description
Your primary responsibilities will include developing methods for the evaluation and improvement of foundation models, as well as refining the data used in training them, applying data centric AI approaches:
Minimum Qualifications
- BS and a minimum of 3 years relevant industry experience
- Work experience in statistical data analysis and machine learning techniques
- Familiar with in-depth analysis and evaluation of machine learning model
- Background with analytical tools such as Jupyter, Pandas, NumPy, Matplotlib
Preferred Qualifications
- Familiarity with various foundation models in GenAI, like VAEs, Transformers, Diffusion Models, NeRF, CLIP, etc.
- Expertise of video analysis algorithms or subjective/objective video quality algorithms, and pioneering technologies
- Expertise in photography, videography, or image science and a passion for learning more
- Experience in training models using frameworks like PyTorch, TensorFlow, Jax, etc.
- Proficient in distributed systems and a good understanding of operating systems
- Expert in at least one scripting language, preferably Python
- Self-motivated and curious with demonstrated creative and critical thinking capabilities and a highly developed aim to improve how things work
- Proficient verbal and written communication skills, along with strong collaborative abilities
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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