Demo

Engineering Manager - Machine Learning

Applied Intuition Inc.
Mountain View, CA Full Time
POSTED ON 2/20/2025
AVAILABLE BEFORE 5/18/2025

About the role

We are looking for an industry leader who will set the technical direction, execute with a team of machine learning and software engineers, and scale a state-of-the-art set of products focused on the development of ML systems. This team is focused on allowing ML developers across autonomy programs to develop performant autonomy models using simulation at scale. You will work directly with customers to solve real-world challenges with simulation, conduct and publish groundbreaking research, and apply the latest in machine learning and software design to our solution. You and your team will be the technical owners of a critical set of revenue-generating products. This position is ideal for a leader with a strong background in ML development for autonomy.

At Applied Intuition, you will :

  • Build a category-leading set of data products
  • Manage a team of talented software engineers working together to solve real-world data and machine learning challenges in autonomy
  • Set the technical direction of the team and ensure your team delivers high-quality software
  • Work directly with top autonomy programs on problems in applied machine learning

We’re looking for someone who has :

  • A MSc or PhD degree in computer science, robotics, or related fields from a top institution
  • 4 years of industry experience in software engineering using C or Python
  • 2 years of experience in leading teams with varied backgrounds
  • Demonstrated experience leading teams in developing cutting-edge software products in the autonomy space
  • Proven track record of picking the right technologies to invest in and seeing complex projects through to the finish line
  • Experience with training ML models and strong ML fundamentals
  • Nice to have :

  • Strong knowledge of common sensor modalities (radar, lidar, and cameras) and perception tasks in autonomy
  • Experience building scalable systems in the cloud
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