Founding Engineer, Computer Vision & Robotics

Clera

United States

On-site

USD 150,000 - 200,000

Full time

7 days ago
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Job summary

Clera is seeking its first engineer to build computer vision systems for manufacturing quality inspection on the factory floor. You will take perception technology from experiments to reliable deployments, helping detect process errors early and paving the way for future robotic automation.

You will address camera motion, occlusion, lighting changes, and motion blur while shipping real-time perception systems and developing reusable infrastructure for field deployments.

Qualifications

  • At least 3 years of experience in computer vision, robotics perception, or applied ML.
  • Strong Python, PyTorch and OpenCV skills for building and debugging models.
  • Experience shipping real-time perception systems under variable lighting and motion.
  • Ownership of an underspecified physical-world problem from end to end.
  • Experience deploying on edge devices and working with field users.
  • Familiarity with synthetic data, Sim2Real, SLAM/VIO, CUDA or TensorRT, ROS2 is a plus.

Responsibilities

  • Build perception systems for detection, tracking, and 3D vision in real-world factory conditions.
  • Address first-person camera challenges such as camera motion, 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.

Skills

Python
PyTorch
OpenCV

Tools

CUDA
TensorRT
ROS2
SLAM

Job description

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 with factory-floor deployments.

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