Founding Engineer, Computer Vision & Robotics (Visa Sponsorship - United States)

Visa sponsorship available. An early-stage physical AI company is building computer vision systems for manufacturing quality inspection. As the first engineer on the perception team, you will take models from experimentation to reliable deployments on factory floors, working directly with the founding team. The role suits someone who wants broad ownership of a hard, underspecified problem in the physical world.

What you'll do

• Build perception systems covering detection, classification, tracking, temporal understanding and 3D vision.
• Develop perception that stays robust to camera motion, changing viewpoints, occlusion, motion blur and variable lighting.
• Improve models, datasets and evaluation by turning failures seen in real deployments into system improvements.
• Implement geometric vision components such as calibration, localization, pose estimation and coordinate transformations.
• Train and debug PyTorch models using both synthetic and real-world data, and optimize low-latency inference for NVIDIA Jetson using TensorRT.
• Spend time on factory floors and turn deployment learnings into reusable perception infrastructure.

What you need

• At least 3 years of experience building and deploying computer vision, robotics perception or applied ML systems beyond a notebook or lab setting.
• Strong Python, PyTorch and OpenCV skills, with experience shipping real-time perception in physical-world conditions.
• Hands-on experience with detection, classification or tracking models and geometric vision.
• Experience deploying perception in production, optimizing for edge or low-latency inference, and working directly with end users or in the field.
• Background in computer vision, robotics, industrial robotics, AR/VR, ADAS perception or physical AI is valued. Experience with synthetic data/Sim2Real, SLAM or VIO, depth estimation, 3D reconstruction, CUDA or TensorRT, ROS2, robot manipulation or robot learning is a plus.
• Ability to take ownership of an underspecified physical-world problem end to end.

How to apply

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