Pivotal
Computer Vision Engineer – Autonomy & Perception
About this role
Pivotal is seeking a Computer Vision Engineer to enhance the perception systems for their eVTOL aircraft, focusing on autonomy capabilities. The role involves collaboration with various engineering teams to develop robust computer vision algorithms for challenging real-world scenarios.
What you'll do
- Design and implement computer vision algorithms
- Support development of perception systems for autonomous platforms
- Collaborate with autonomy and AI engineers
- Focus on object detection and tracking
- Contribute to navigation and obstacle avoidance
- Enhance mission autonomy capabilities
What they're looking for
- Strong fundamentals in computer vision
- Experience with perception systems
- Knowledge of robotic platforms
- Proficiency in AI and machine learning
- Understanding of sensor fusion techniques
- Ability to work in dynamic environments
- Expertise in visual localization
- Problem-solving skills
Benefits
- Opportunity for growth in a cutting-edge industry
- Work on innovative eVTOL aircraft
- Collaborative team environment
- Involvement in next-generation technology
- Adventurous company culture
- Impact on future mobility solutions
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Pivotal
Pivotal develops innovative electric Vertical Takeoff and Landing (eVTOL) aircraft, including models like BlackFly and Helix, with a focus on aeromechanical systems, flight control, and autonomous capabilities. The company is hiring verification engineers, quality engineers, motor controls engineers, firmware engineers, and computer vision engineers to advance aircraft performance, safety, manufacturing quality, and autonomous perception systems.
View all jobs at PivotalLikely interview questions
- Walk us through a computer vision project you've deployed on an autonomous or robotic platform. What were the biggest challenges in moving from prototype to production?
- How have you approached real-time object detection and tracking in dynamic environments, and what trade-offs did you consider between accuracy and latency?