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Apple HID-Applied Machine Learning Engineer 
United States, California, Cupertino 
829344053

01.06.2024
Description
Work cross-functionally with sensor architects and software engineers to build the next generation of sensing technologies.
Key Qualifications
  • Strong background in Deep Learning and classical Machine Learning, including but not limited to CNN/RNN architectures, GAN, active learning, k-shot learning and model complexity reduction techniques.
  • Track record of coming up with new ML ideas, as shown by publications, patents or open-source projects
  • Experience with one or more Deep Learning packages including but not limited to TensorFlow and PyTorch
  • Proficiency in Python programming
Education & Experience
Ph.D. degree in CS (preferred), or other STEM fields such as EE, or Statistics.M.S. in CS with at least 2 years of experience in research and development of deep learning algorithms.
Additional Requirements
  • Experience in human computer interaction (HCI) space
  • Experience with signal processing
  • Familiarity with C++ or Objective-C programming
  • Experience processing large-scale data sets using Mesos, Spark, or Hadoop
  • Past experience in creating high-performance implementations of deep learning algorithms
  • Experience developing software for Augmented Reality (AR) / Virtual Reality (VR) (ex. ARKit)
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 $138,900 and $256,500, 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.