Ml/Cv Engineering Manager

ML/CV Engineering Manager Overview

• Lead a dynamic team as an ML/CV Engineering Manager, driving innovation in autonomous systems and real-time detection technologies.
• Collaborate with senior leadership to define technical roadmaps and ensure successful execution of strategic goals.
• Contribute directly to the development of cutting-edge computer vision algorithms for critical applications.
• Manage and grow a team of engineers, fostering career development and technical excellence.
• Drive engineering quality through rigorous testing, validation, and design reviews.
• Work in a fast-paced, engineering-first environment focused on real-world impact and innovation.
• Engage in hands-on development while transitioning to a broader leadership role as the team scales.
• Support integration milestones with hardware and electrical engineering teams for seamless system functionality.

ML/CV Engineering Manager Key Responsibilities & Duties

• Define and execute technical roadmaps for computer vision and machine learning systems.
• Lead and mentor a team of engineers, ensuring technical growth and career development.
• Develop and optimize algorithms for real-time detection, tracking, and classification.
• Coordinate integration milestones with hardware and electrical engineering teams.
• Conduct design and code reviews to maintain high engineering standards.
• Collaborate with senior leadership to align team priorities with organizational goals.
• Drive system validation across diverse environmental and operational scenarios.
• Support hiring initiatives to scale the team effectively.

ML/CV Engineering Manager Job Requirements

• Master’s degree in Computer Science, Electrical Engineering, or related field.
• 10+ years of experience in machine learning and computer vision, ideally in robotics or autonomous systems.
• Proficiency in Python and C++, with expertise in frameworks like TensorFlow or PyTorch.
• Experience managing engineering teams and defining technical roadmaps.
• Knowledge of embedded systems and sensor integration, including LiDAR and RADAR.
• Strong background in real-time or safety-critical applications deployment.
• Preferred Ph.D. in computer vision, machine learning, or related field.
• Experience with multi-sensor fusion and field testing cycles.