Machine Learning/Computer Vision Engineering Manager Overview
- Lead the development of cutting-edge autonomous systems leveraging machine learning and computer vision technologies.
- Manage and grow a team of engineers, fostering technical excellence and innovation.
- Collaborate with senior leadership to define and execute technical roadmaps.
- Contribute to real-time detection, tracking, and classification algorithm development.
- Ensure system reliability through rigorous testing and validation processes.
- Drive the integration of CV/ML technologies with hardware systems.
- Support the transition from prototype to field-ready platforms.
- Promote a culture of innovation and technical excellence within the team.
Machine Learning/Computer Vision Engineering Manager Key Responsibilities & Duties
- Lead and expand a team of engineers specializing in CV/ML technologies.
- Define technical roadmaps and align team priorities with organizational goals.
- Develop and optimize algorithms for real-time autonomous systems.
- Coordinate integration milestones with hardware and electrical engineering teams.
- Conduct design and code reviews to maintain engineering quality.
- Drive testing and validation across diverse operational scenarios.
- Collaborate with leadership to refine technical direction and execution strategies.
- Support field testing and hardware integration cycles for system deployment.
Machine Learning/Computer Vision Engineering Manager Job Requirements
- Master’s degree in Computer Science, Electrical Engineering, or related field.
- 10+ years of experience in machine learning-based computer vision and signal processing.
- Proficiency in Python, C++, and machine learning frameworks like TensorFlow or PyTorch.
- Experience managing or leading technical teams in CV/ML engineering.
- Strong background in deploying CV systems in real-time or safety-critical applications.
- Familiarity with embedded systems and sensor integration technologies.
- Expertise in object detection and tracking under challenging conditions.
- Experience with multi-sensor fusion and edge hardware optimization.
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