ISEE builds self-driving yard trucks for logistics hubs using Nvidia Jetson, PyTorch, and LiDAR-based sensor fusion. The stack reveals heavy embedded systems focus (Embedded C, STM32, CAN protocols) paired with modern ML frameworks—a design pattern for real-time, safety-critical autonomy. Pain points around ISO 26262 compliance and safety-critical software, combined with active deployment cycles, indicate ISEE is transitioning from R&D into production operations, with engineering resources concentrated on vehicle systems and ops teams scaling customer launches.
Tech Stack 31 technologies
Core StackMATLAB Python Linux Jira Slack C++ PyTorch TensorFlow AWS Solidworks Nvidia Jetson Embedded C Git Google Workspace STM32 CAN CANopen I2C Caffe LiDAR GNSS ROS Fusion 360 Inventor C/C++ NX USB RS-232 GPS Vector CANoe
What ISEE Is Building
◆Challenges
- Vehicle compliance
- Improve hardware engineering workflows
- Replacing legacy subsystems
- Meeting aggressive goals
- Autonomous system deployments
- Autonomous system deployment integration
- Customer operations coordination
- Ensuring compliance with iso 26262
- Developing safety-critical software systems
▲Active Projects
- Launch autonomous yard trucks in customer yards
- Deployment & operations launch of autonomous yard trucks
- Support full software development life cycle for autonomous vehicle software stack
- Operations and development of software and hardware on autonomous yard trucks
- Autonomous vehicle systems for shipping and logistics
- Autonomous system deployments
- Multimodal sensor fusion and calibration system
- Simulation discrepancy analysis for autonomous vehicle systems
- Robust computer vision algorithms on isee’s vehicle fleet
- On-site demos, pilots, pocs, launches
Hiring Activity
Minimal35 roles · 0 in 30d
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