Multisensor Perception Engineer (SLAM & Fusion)

Movendi Inc.

San Francisco (CA)

Hybrid

USD 120,000 - 180,000

Full time

32 hours ago
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Job summary

Movendi Inc. in San Francisco develops perception systems for industrial robotics and automation. You will transform raw sensor data into structured world representations and build robust multi-sensor fusion pipelines.

Responsibilities include designing object detection, segmentation and tracking, developing scene understanding, and integrating perception with robotics platforms. Experience with ROS2 and PyTorch is valued, with a focus on real-world industrial environments.

Qualifications

  • Experience with sensor fusion and multi-sensor integration.
  • Familiarity with SLAM, perception, and mapping.
  • Strong background in deep learning with PyTorch; OpenCV experience.
  • ROS2 experience required.
  • Industrial vision systems experience is a plus.

Responsibilities

  • Transform raw sensor data into meaningful representations of the physical world.
  • Develop perception pipelines for cameras, LiDAR, radar, IMUs, and industrial sensors.
  • Build multi-sensor fusion algorithms.
  • Design object detection, segmentation, and tracking systems.
  • Develop scene understanding and environmental mapping.
  • Improve robustness under noisy and changing industrial conditions.
  • Generate structured world representations consumable by Reality Models.
  • Integrate perception pipelines with robotics and automation systems.

Skills

Sensor fusion
SLAM
Deep learning
PyTorch
OpenCV
ROS2
Industrial vision systems

Tools

PyTorch
OpenCV

Job description

Movendi Inc. in San Francisco develops perception systems for industrial robotics and automation. You will transform raw sensor data into structured world representations and build robust multi-sensor fusion pipelines.

Responsibilities include designing object detection, segmentation and tracking, developing scene understanding, and integrating perception with robotics platforms. Experience with ROS2 and PyTorch is valued, with a focus on real-world industrial environments.

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