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 Senior Deep Learning Engineer, Body Technologies - Cupertino, California, United States

   
Job information
Posted by: Apple 
Hiring entity type: Retail 
Work authorization: Not Specified for United States
Position type: Direct Hire, Full-Time 
Compensation: ******
Benefits: See below
Relocation: Not specified 
Position functions: Computers - Programming Languages
Computers - Platforms
Computers - Software Engineer
Computers - Webmaster
 
Travel: Unspecified 
Accept candidates: from anywhere 
Languages: English - Fluent
 
Minimum education: See below 
Minimum years experience: See below 
Resumes accepted in: English
Cover letter: No cover letter requested
Job code: 200307350 / Latpro-3836472 
Date posted: Jan-18-2022
State, Zip: California, 95014

Description

Senior Deep Learning Engineer, Body Technologies

Santa Clara Valley (Cupertino) , California , United States

Machine Learning and AI

Summary

Posted: Oct 28, 2021

Role Number: 200307350

Have you devised breakthrough deep learning solutions to long-standing computer vision problems? Are you ready to collaborate with Apple teams to bring your knowledge to market and make a far-reaching impact? We are seeking a driven and dedicated machine learning engineer with a background in human body pose estimation, object detection and tracking, or a related field. As a member of the team you will tackle real world problems with direct impact on new and innovative products in the space of Virtual and Augmented Reality that will delight and inspire millions of people every day. We are looking for a strong deep learning engineer with industry experience or a proven academic track record.

Key Qualifications

  • 3+ years of experience with deep learning with excellent understanding of deep learning fundamentals.
  • Strong track record of academic publications or successful industry projects related to computer vision and deep learning.
  • In depth understanding of 2D and 3D Computer Vision.
  • In depth understanding of Machine Learning principles.
  • Experience in working with large real world datasets.
  • Proficiency with Python (C++, Objective-C, Swift are a plus).
  • Proven experience in at least one major machine learning framework: TensorFlow, Keras, (Py-)Torch etc.

Description

We are the "Body Technology" team that is responsible for many of the key algorithms for videos and photos on Apple products (e.g iPhone, iPad and more), provide backbone algorithms for ARKit, and conduct research and development in the space of Virtual and Augmented Reality. We build people understanding algorithms that drive features such as ARKit Motion Capture. We are looking for smart engineers who are passionate about building products for millions of customers around the world. You will be working on ground breaking technology and develop algorithms that enable a high-quality user experience across a range of applications. As a part of our team, you will collaborate with both software teams (computer graphics, video engineering, data generation/annotation, system integration) and hardware engineers (cameras, silicon, electrical engineering, product design). Join us for the rare opportunity to work on novel algorithms software that go beyond the state of the art and eventually will touch the lives of millions of people around the world!

Education & Experience

MS/PhD in Machine Learning, Computer Science, Mathematics, or a related field. Alternatively, a comparable industry career with a proven track record. If this is you, we'd love to hear from you!

Additional Requirements

  • * Experience with temporal deep learning techniques, domain adaptation or generalization techniques for running deep learning models in unconstrained real world scenarios
  • * Experience working on real world problems and large datasets
  • * Excitement for solving problems in new and creative ways but also about bringing research projects to product quality
  • * Aspiration to stay on top of the state of the art and the latest developments in the research community





Requirements

See job description

 

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