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Home›Jobs›Apple›ML Research Scientist
Apple

About Apple

The personal technology company redefining user experience

🏢 Tech, Hardware👥 1001+ employees📅 Founded 1976📍 Cupertino, CA⭐ 4.2
B2CB2BHardwareSaaSTelecommunicationseCommerce

Key Highlights

  • Market cap of $3 trillion as of 2022
  • Over 1 billion active devices worldwide
  • Comprehensive medical plans including mental healthcare
  • Paid parental leave and gradual return-to-work program

Apple Inc. (NASDAQ: AAPL), headquartered in Cupertino, CA, is the world's most valuable company with a market capitalization of $3 trillion as of 2022. Known for its iconic products such as the iPhone, iPad, and Mac, Apple serves over 1 billion active devices globally. The company has a strong commi...

🎁 Benefits

Apple offers comprehensive medical plans covering physical and mental healthcare, paid parental leave, and a gradual return-to-work program. Employees...

🌟 Culture

Apple's culture emphasizes an obsessive focus on user experience and consumer privacy, setting it apart from competitors. The company promotes inclusi...

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Apple

ML Research Scientist

Apple • Santa Clara, California, United States

Posted 1 month ago🏛️ On-SiteMid-LevelResearch scientist📍 Santa clara
Apply Now →

Skills & Technologies

Machine learningRoboticsReinforcement learningImitation learningVision-language-action modelsFoundation models

Job Description

Our team is looking for a researcher to work with a collaborative group of ML and robotics specialists within Apple’s organization. This team is working to leverage foundation models and build upon cutting-edge ML research to enable the development of generalizable and reliable robot systems. Duties associated with this position include conducting collaborative research on a variety of novel machine learning approaches and robotics platforms. A core component of such duties will include leveraging foundation models in the development of brand new learning-based control policies. Particular areas of interest include, but are not limited to, vision-language-action models (VLAs), imitation learning, reinforcement learning, world models, synthetic data and related areas. Part of this development will include informing data collection efforts, contributing to training and testing infrastructure, and remaining up to date on the latest literature.

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