Engineering Manager, Lime Vision

Lime Lime · Consumer · United States · Engineering

Engineering Manager for Computer Vision at Lime, leading a team to develop and deploy advanced computer vision and ML solutions for intelligent transportation systems. The role involves technical leadership, people management, driving strategy, and overseeing the full ML lifecycle from concept to production, with a focus on embedded and edge AI.

What you'd actually do

  1. Lead, mentor and develop a high-performing team of computer vision and machine learning engineers.
  2. Own the business outcomes of the Computer Vision team and align technical investments with company objectives.
  3. Drive architecture, system design, and technical direction for scalable, reliable computer vision and machine learning solutions.
  4. Oversee the end-to-end ML lifecycle, including model development, deployment, monitoring, and continuous improvement.
  5. Partner with Hardware and Firmware teams to optimize solutions for embedded and edge computing platforms.

Skills

Required

  • 3+ years of experience leading and developing engineering teams
  • 8+ years of experience in Computer Vision, Machine Learning, Artificial Intelligence, or related fields
  • Strong technical depth in computer vision, machine learning, system design, and software engineering
  • Experience driving technical strategy, architecture, and business outcomes across cross-functional teams
  • Experience building, deploying, and operating ML systems in production, including edge AI environments and modern MLOps practices
  • Bachelor’s degree in Computer Science, Electrical Engineering, Robotics, or a related technical field

Nice to have

  • Master’s degree or PhD in Computer Vision, Machine Learning, Artificial Intelligence, Robotics, or a related discipline

What the JD emphasized

  • proven track record of delivering production-grade systems
  • Experience building, deploying, and operating ML systems in production, including edge AI environments and modern MLOps practices

Other signals

  • leading a team of computer vision and machine learning engineers
  • driving architecture, system design, and technical direction for scalable, reliable computer vision and machine learning solutions
  • overseeing the end-to-end ML lifecycle, including model development, deployment, monitoring, and continuous improvement
  • partnering with Hardware and Firmware teams to optimize solutions for embedded and edge computing platforms