
Imagry makes it possible for vehicles and buses to drive themselves using vision-first AI, without HD maps or expensive sensors. The company delivers a hardware-agnostic, B2B SaaS autonomy stack (Imagry Cortex) that uses deep learning, computer vision, imitation learning, and neural networks to perceive environments and learn driving behavior in real time. The platform targets automotive OEMs, tier-1 suppliers, and public transit operators and integrates with existing vehicle platforms and fleet systems. Imagry’s mapless SAE L3/L4 software is deployed in public transit and passenger vehicle pilots and is positioned to scale across fleets and transit networks globally.

Imagry makes it possible for vehicles and buses to drive themselves using vision-first AI, without HD maps or expensive sensors. The company delivers a hardware-agnostic, B2B SaaS autonomy stack (Imagry Cortex) that uses deep learning, computer vision, imitation learning, and neural networks to perceive environments and learn driving behavior in real time. The platform targets automotive OEMs, tier-1 suppliers, and public transit operators and integrates with existing vehicle platforms and fleet systems. Imagry’s mapless SAE L3/L4 software is deployed in public transit and passenger vehicle pilots and is positioned to scale across fleets and transit networks globally.
Imagry is seeking an experienced Machine\Deep Learning Engineer
4-5 years of experience in the Industry.
On-Site in San Jose California!
The engineer will work on devising, developing, and integrating novel deep vision-based algorithms at the heart of our autonomous driving system.
This is an opportunity to work on cutting-edge deep vision methods, develop and innovate new ideas, and see them deployed on the road in our autonomous vehicles.
Responsibilities:
Designing, training, and optimizing novel computer vision algorithms.
Taking an End-to End responsibility over medium to long-term projects
Seeing projects through from research to deployment
Devising and implementing performance metrics
Supervising complex data pipelines, from the collection stage, through annotation, to training models
Communicating, presenting and visualizing results
Aligning the algorithms with the product needs