Gather AI - Staff Autonomy Engineer (MHE Vision)
Requirements
• 7+ years of software engineering experience with at least 3 years building autonomy, robotics, or perception systems that run on physical hardware in production • Strong C++ and Python, with experience writing software that runs on embedded or resource-constrained compute platforms • Hands-on experience with computer vision pipelines including camera-based detection, localization, or tracking in real-world environments • Experience building and debugging software on deployed systems, including log-based diagnosis and field iteration • Comfort operating at Staff level: setting technical direction, making consequential architecture decisions, and taking end-to-end ownership of a system • Experience with ROS2 or similar robotics middleware in a production context • Background in warehouse, logistics, or industrial automation environments • Familiarity with sensor fusion, calibration, or multi-camera systems • Experience with SLAM, odometry, or other localization approaches on mobile platforms
Responsibilities
• Own the autonomy software stack for Gather products, including perception, localization, and inventory scanning pipelines running on vehicle-mounted embedded compute • Contribute to the architecture of the product autonomy stack as the product scales to more vehicle types, facility layouts, and customer configurations • Document existing systems • Plan future revisions • Contribute and lead to design improvements and testing • Solve problems across the autonomy stack • Design and implement algorithms that reliably detect, identify, and record inventory locations as forklifts and drones move through warehouse aisles under variable lighting, occlusion, and motion • Improve scan accuracy and coverage through algorithmic improvements, better sensor integration, and tighter coordination with the ML perception models • Build and maintain the software systems that manage scan triggering, location mapping, and data handoff to the cloud platform • Collaborate with hardware and field teams to diagnose and resolve issues on deployed systems, using log data and field observations to drive improvements
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