Propel

Propel

Computer Vision Engineer - Robotics Start Up

Pasadena, California, USFull-time6 days agovia LinkedIn

Salary

-

Job type

Full-time

Location

Pasadena, California, US

Remote

No

Posted

6 days ago

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Job description

🤖Senior Computer Vision Engineer

📍 Pasadena, California |

đź’˛175,000 + Competitive Package

A Senior Computer Vision Engineer for an innovative technology company building intelligent automated systems at the cutting edge of robotics and machine perception.

âś…The Role

This is a hands on engineering position with genuine ownership of how an advanced automated platform perceives and interprets its environment. You will take full responsibility across the computer vision and perception stack, designing and implementing algorithms that interpret visual data from cameras, depth sensors, and other imaging inputs to produce reliable, production grade systems.

You will take full ownership of the vision pipeline spanning installation, calibration, performance optimisation, and iterative improvement across each release cycle. They will evaluate and benchmark third party algorithms and models, driving quality through rigorous automated testing and contributing to the research direction of the team.

🎯Requirements

  • 5 or more years of industry experience delivering production quality computer vision systems, with a strong portfolio of shipped perception or imaging products.
  • Deep expertise in core computer vision algorithms including feature extraction, keypoint detection, homography estimation, stereo vision, and optical flow.
  • Proficiency in object detection and classification architectures (YOLO, Faster RCNN, EfficientDet) and experience training and fine tuning models on custom datasets.
  • Strong command of tracking algorithms including Kalman filtering, SORT, and DeepSORT, with practical experience deploying multi object tracking in real world environments.
  • Expertise in 3D pose estimation techniques including PnP solvers, ICP registration, and visual odometry, with hands on experience deploying these in production.
  • Camera calibration experience covering intrinsic and extrinsic parameters, lens distortion modelling, and multi camera rig calibration.
  • Hands on machine learning experience with PyTorch or TensorFlow, including CNN architecture design, dataset curation and augmentation, model evaluation, and deployment optimisation.
  • Sensor fusion knowledge integrating camera, lidar, and depth sensor inputs, with a strong grasp of coordinate frame transforms and spatial reasoning.
  • Proficiency in Python and C++, with experience building and optimising vision pipelines in Linux and Docker environments.
  • Familiarity with ROS and experience integrating computer vision systems with robotic or embedded platforms.
  • Bachelor's degree or above in Computer Vision, Electrical Engineering, Computer Science, or a related technical field.
  • âť—Bonus Points: Experience with neural radiance fields (NeRF), transformer based vision models (ViT, DETR), or on device model optimisation using TensorRT or ONNX would be a significant advantage.

🏠The Company

This is an ambitious technology company developing next generation automated systems that operate in complex, dynamic environments. They are a team of deep technical specialists who value intellectual rigour and take pride in building things that genuinely work.

đź’»Next Steps

If you are a Computer Vision Engineer looking for a technically challenging role where your algorithmic expertise will have a direct impact please share your CV by clicking apply. Any questions please reach out!

Responsibilities

  • This is a hands on engineering position with genuine ownership of how an advanced automated platform perceives and interprets its environment
  • You will take full responsibility across the computer vision and perception stack, designing and implementing algorithms that interpret visual data from cameras, depth sensors, and other imaging inputs to produce reliable, production grade systems
  • You will take full ownership of the vision pipeline spanning installation, calibration, performance optimisation, and iterative improvement across each release cycle
  • They will evaluate and benchmark third party algorithms and models, driving quality through rigorous automated testing and contributing to the research direction of the team

Qualifications

  • 5 or more years of industry experience delivering production quality computer vision systems, with a strong portfolio of shipped perception or imaging products
  • Deep expertise in core computer vision algorithms including feature extraction, keypoint detection, homography estimation, stereo vision, and optical flow
  • Proficiency in object detection and classification architectures (YOLO, Faster RCNN, EfficientDet) and experience training and fine tuning models on custom datasets
  • Strong command of tracking algorithms including Kalman filtering, SORT, and DeepSORT, with practical experience deploying multi object tracking in real world environments
  • Expertise in 3D pose estimation techniques including PnP solvers, ICP registration, and visual odometry, with hands on experience deploying these in production
  • Camera calibration experience covering intrinsic and extrinsic parameters, lens distortion modelling, and multi camera rig calibration
  • Hands on machine learning experience with PyTorch or TensorFlow, including CNN architecture design, dataset curation and augmentation, model evaluation, and deployment optimisation
  • Sensor fusion knowledge integrating camera, lidar, and depth sensor inputs, with a strong grasp of coordinate frame transforms and spatial reasoning
  • Proficiency in Python and C++, with experience building and optimising vision pipelines in Linux and Docker environments
  • Familiarity with ROS and experience integrating computer vision systems with robotic or embedded platforms
  • Bachelor's degree or above in Computer Vision, Electrical Engineering, Computer Science, or a related technical field
  • âť—Bonus Points: Experience with neural radiance fields (NeRF), transformer based vision models (ViT, DETR), or on device model optimisation using TensorRT or ONNX would be a significant advantage

Benefits

  • đź’˛175,000 + Competitive Package

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