Sr. Software and Computer Vision Engineer
The Sr. Software and Computer Vision Engineer is responsible for designing and implementing software systems to improve our computer vision applications. Some of their responsibilities include updating current software systems, making improvement suggestions, collaborating with analysts and designers, testing applications, documenting procedures, writing training manuals, and making sure projects are completed in time and within budget. Job Description.
Let’s Make Our Roads Safer
- Experience: 4 to 8 plus years of technology development and team management
- Education: BS/MS/Ph.D. in Robotics in Artificial Intelligence, Statistics, Information Systems, Software Engineering, Computer Science, Electrical Engineering, Applied Math, Physics, or the equivalent in experience and evidence of exceptional ability. A Ph.D. in developing deep learning and machine learning models is a plus.
- Technical Skills: Proficiency in the development of software development, machine learning, and deep learning.
- Hardware and Sensor Technology: A plus if you have experience with IoT edge devices (NVIDIA), LiDAR, camera, RADAR, and sensor fusion.
- Location: Scottsdale, AZ / remote
- Department: Technology and Product Development
- Coding Languages: Must have a minimum of five years in C++ and/or Python
- Reports to: President and Director of Information Technology
- Type of Employment: Share options, PTE, and FTE
- Travel Requirements: Occasional, as needed
TO APPLY FOR THE POSITION, PLEASE COMPLETE OUR ONLINE APPLICATION.
Sensagrate is developing a smart city and smart infrastructure platform that provides real-time data to support intelligent decision-making for smart city use cases. The platform is a computer vision perception AI software and reporting called SensaVision developed from deep learning algorithms for cameras (2D) and LIDAR (3D) data. We use the 2D and 3D visual data to merge into a process called sensor fusion. The software detects, classifies, and tracks motorized and non-motorized objects in real-time and aggregates the data for analytics and reporting. We target a 95% object detection accuracy rating as our algorithms continue to learn. Smart city and infrastructure applications can improve some key quality-of-life indicators by 10% to 30%. Review our fact sheet.