May Mobility

May Mobility

Lead Machine Learning Engineer

Remote, USA

Sponsorship not specified$220k-$270kDetected 82 days ago
PythonC++LinuxMachine LearningPyTorchRoboticsSensorsResearchMentoring

About the role

  • Since our founding in 2017, we've given more than 500,000 autonomous rides to real people around the globe.
  • Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification.
  • You may be the perfect candidate for this or another role at May.

Responsibilities

  • Domestic partners who have been residing together at least one year are also eligible to participate.
  • Design, train and evaluate state of the art models for May's autonomous driving, simulation and ML Platform stack.
  • Lead small teams of cross functional Engineers beyond the state of the art.

Requirements

  • Travel required? -
  • Travel required? - Low: 5%-10%
  • Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

Compensation

  • $220,000 - $270,000 USD
  • Furthermore, May Mobility does not pay placement fees for candidates submitted by any agency other than its approved partners.

Benefits

  • Comprehensive healthcare suite including medical, dental, vision, life, and disability plans.
  • Health Savings and Flexible Spending Healthcare and Dependent Care Accounts available.
  • Rich retirement benefits, including an immediately vested employer safe harbor match.
  • Generous paid parental leave as well as a phased return to work.
  • Flexible vacation policy in addition to paid company holidays.
  • Total Wellness Program providing numerous resources for overall wellbeing
  • Leverage emerging techniques in the End-to-End driving, Vision Language Action (VLA), World or Foundation model domains to solve commercial-scale problems.
  • Vision Language Action Models

Company info

  • Want to learn more about our culture & benefits?

This listing is sourced directly from May Mobility's careers page and normalized into a canonical job model.