Founding CV Engineer: Real-Time Robotics & Edge AI

Clera; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Visa sponsorship available

Job summary

Clera is building computer vision systems for quality inspection in manufacturing. You will join an early engineering team to advance perception from experimentation to reliable factory deployments, tackling challenges like camera motion, occlusion, and lighting changes.

You will ship real-time perception systems, optimize inference speed, and deploy edge solutions in factory environments, collaborating with users to drive model and data improvements.

Qualifications

• Minimum 3 years building and deploying computer vision or robotics perception systems outside notebooks or labs.
• Experience shipping real-time perception systems in changing real-world conditions.
• Strong Python, PyTorch, and OpenCV skills with experience in detection/classification/tracking models.

Responsibilities

• Build perception systems for detection, classification, tracking, temporal understanding, and 3D vision.
• Address camera motion, occlusion, motion blur, and lighting variations.
• Collaborate with the team to turn field failures into model and data improvements.
• Develop geometric vision capabilities including calibration, localization, pose estimation, and transforms.
• Train and debug PyTorch models on synthetic and real-world data.
• Optimize low-latency inference and deploy edge solutions in factory environments.

Skills

Python

PyTorch

OpenCV

3D vision

Edge deployment

Sim2Real

CUDA/TensorRT

ROS2

Tools

CUDA

TensorRT

ROS2

Job description

Clera is building computer vision systems for quality inspection in manufacturing. You will join an early engineering team to advance perception from experimentation to reliable factory deployments, tackling challenges like camera motion, occlusion, and lighting changes.

You will ship real-time perception systems, optimize inference speed, and deploy edge solutions in factory environments, collaborating with users to drive model and data improvements.