About the Role
Join the founding team of an industrial automation startup as its first engineer, building computer vision systems for manufacturing quality inspection. You will take perception technology from experiments to reliable deployments on factory floors, helping detect process errors early and laying groundwork for future robotic automation.
What You'll Do
Build perception systems for detection, classification, tracking, temporal understanding, and 3D vision in real-world factory conditions.
Address first-person camera challenges such as camera motion, changing viewpoints, occlusion, motion blur, and varying lighting.
Work with the technical leadership team to turn deployment failures into improvements to models, data, and evaluation.
Build geometric vision capabilities including calibration, localization, pose estimation, and coordinate transformations.
Train and debug PyTorch models on synthetic and real-world data, and optimize low-latency inference for edge deployment.
Deploy systems on factory floors and develop reusable perception infrastructure from field learnings.
What We're Looking For
At least 3 years of experience in computer vision, robotics perception, or applied machine learning, including building and deploying perception systems beyond notebooks or lab environments.
Strong Python, PyTorch, and OpenCV skills, with experience building and debugging detection, classification, or tracking models.
Experience shipping real-time perception systems that perform reliably amid changing lighting, occlusion, motion blur, or camera motion.
Experience with geometric vision and ownership of an underspecified physical-world problem from end to end.
Experience deploying on edge devices or optimizing production inference, and working directly with users or in field deployments.
Experience with synthetic data and Sim2Real, SLAM or VIO, depth or 3D reconstruction, CUDA or TensorRT, ROS2, or robot learning is beneficial.
Compensation & Benefits
Base salary is $150,000 to $200,000 USD annually. Visa sponsorship is available.
Location
On-site in the United States, with factory-floor deployments.