Staff Machine Learning Engineer

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Stack AV

πŸ“Remote - United States

Job highlights

Summary

Join Stack's Static World Perception team as a Staff or Senior Staff Engineer and lead the development and improvement of algorithms and systems for online and offline mapping in L4 self-driving vehicles. You will integrate various data sources (LiDAR, cameras, GPS, etc.) to generate road elements governing traffic flow. This role requires extensive experience in deep learning model deployment, applied research, and perception/mapping solutions for real-time robotic applications. You will contribute to the team's technical direction, write robust software, drive cross-functional features, and proactively identify system limitations. Strong software engineering, machine learning, and communication skills are essential.

Requirements

  • Extensive experience architecting, training, and deploying deep learning models into real-world environments
  • Track record of driving applied research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization
  • Experience delivering perception/mapping solutions for real-time robotic applications
  • Strong experience in software engineering and machine learning algorithm design
  • Fluency in Python
  • Experience with C++

Responsibilities

  • Work in the team to deliver state-of-the-art online and offline mapping algorithms and systems for L4 self-driving vehicles
  • Contribute to the technical direction of the team and broader perception org
  • Write robust software for real-time, resource-constrained, safety-critical applications
  • Drive highly cross-functional features and work streams from problem statement to delivery
  • Proactively identify limitations with the existing online mapping system and drive improvements
  • Align stakeholders with strong presentation and communication skills
  • Lead challenging technical topics and build consensus

Preferred Qualifications

Prior experience in sensor fusion and/or online mapping is highly desirable

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