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Data Labeling Manager

Menlo Park, CA

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Expected Salary
$21.00 - $25.00 per hour
Job Description

HireArt is seeking a bright, detail-oriented self-starter to work at’s Menlo Park office on a contract basis. As Data Labeling Manager, you’ll review and label large bodies of data, while ensuring the quality of data-sets and providing insights that improve process efficiency. 

We’re seeking candidates who are passionate about’s mission to develop safe, reliable algorithmic solutions for autonomous navigation — guiding technologies from robots to vehicles. 

You also have meticulous attention to detail, a strong work ethic and high level of focus, technical aptitude, and excellent communication skills. A valid driver’s license is also required. 

This is a part-time role, starting at 3 days per week, with the potential to grow into a full-time, leadership position. 


  • Label relevant objects and segments in images using our internal labeling software.
  • Watch various driving scenario videos and provide tagging information such as road, weather and lighting conditions. 
  • Working with the rest of the team to ensure the quality of data-sets.
  • Managing remote labeling teams via Skype.
  • Interacting with Research and Development personnel to communicate findings about perception system performance and about efficiency improvements to labeling tools.
  • Technical aptitude with strong computer skills
  • Able to remain focused on repetitive computer-based tasks and maintain good work ethic
  • A high level of attention to detail
  • Strong verbal and written communication 
  • Valid driver’s license

Commitment: This is a 12-month contract with potential for extension. The position is part-time (3 days per week) with the opportunity to grow into a full-time role. 

Company Description is a team of elite mathematicians and engineers, building the next generation of AI algorithms for autonomous driving. Our goal is enabling widespread L4/L5 autonomy - that is, algorithmic vehicular navigation that functions across many environments, at beyond the safety level of humans, yet completely independent from any human input. Our approach leverages a novel combination of tools from applied mathematics and deep learning to create truly scalable learning systems that do not rely on human annotation across the full spectrum of autonomy, including vision-based perception, sensor fusion, mapping, path planning and control.

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