Robotics & Computer Vision Integration Engineer

TSMG
Lviv, Ukraine
On-site

Who this role is best for

Aimed at mid-to-senior level engineers with cross-disciplinary experience in robotics and computer vision who are comfortable with hands-on integration and validation.

Best fit for

  • Candidates with experience in sensor integration and ROS/ROS2 pipelines for robotics data collection
    — “Configure and maintain ROS/ROS2 pipelines for sensor integration and data collection.”
  • Individuals who have worked with RGB/RGB-D cameras and depth sensors in real-world systems
    — “Experience working with RGB/RGB-D cameras and depth sensors.”

Things to consider

  • The role demands hands-on work with both hardware and software components simultaneously
    — “Comfortable working in a Linux environment.”
  • A strong emphasis is placed on data quality and system validation across the sensor-to-dataset pipeline
    — “Validate sensor data quality, timestamps, coordinate transforms, and system performance.”

How to stand out

  • Highlight experience with multi-sensor synchronization and system validation in your resume and interview responses
    — “Configure and validate multi-sensor systems for synchronized data capture.”
  • Emphasize your background in ROS/ROS2 and data logging using formats like ROS bags or MCAP
    — “Implement and maintain data logging workflows using ROS bags, MCAP, or similar formats.”
  • Showcase your ability to troubleshoot hardware, software, and networking issues in robotics systems
    — “Troubleshoot hardware, software, networking, and synchronization issues across the collection setup.”
  • Demonstrate your skills in Python and/or C++ for system integration and data validation
    — “Develop tools and scripts in Python and/or C++ for system integration, testing, and data validation.”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • reliable data collection systems
  • synchronized sensor systems
  • repeatable data collection workflows
Typical background
robotics engineeringcomputer visionsensor integration

Skills & requirements

Required

RoboticsComputer VisionSensor IntegrationData CollectionRos/ros2Camera CalibrationCoordinate Transformations

Preferred

Oak-dIntel RealsenseZED CamerasVIVE / Steamvr / OpenxrMCAPSlam/vio Pipelines

Stack & domain

PythonC++ROSROS2Rgb/rgb-d CamerasDepth SensorsImus6dof Tracking SystemsRobotic Or Handheld GrippersCamera CalibrationCoordinate TransformationsSLAMVIOLinuxGitStandard Software Development WorkflowsOak-dIntel RealsenseZED CamerasVIVESteamvrOpenxrMCAPRobotic Manipulators Or GrippersMulti-camera SynchronizationHand Tracking Or Motion Capture SystemsSlam/vio PipelinesEmbodied AIRobot LearningManipulation DatasetsTroubleshootingProblem-solvingCollaborationDocumentationRoboticsComputer VisionSensor IntegrationData CollectionAI

About the role

Original posting from TSMG via Lever

Project Overview

We are building a robotics data collection lab focused on bimanual manipulation and embodied AI datasets. We are looking for a Robotics & Computer Vision Integration Engineer to help design, integrate, and validate synchronized sensor and robotics data collection systems.

In this role, you will work with cameras, depth sensors, IMUs, 6DoF tracking systems, and robotic or handheld grippers to build reliable, repeatable data collection rigs. You will be responsible for hardware and software integration, sensor calibration and synchronization, coordinate-frame alignment, data logging, and collection workflow validation.

What We’re Looking For

We are looking for someone who is comfortable moving between hardware integration, robotics software, and data quality validation. The ideal candidate is hands-on, technically curious, and able to diagnose problems across the full sensor-to-dataset pipeline.

Experience building or maintaining real-world robotics systems is especially valuable, as the role requires turning individual sensors and components into a stable, synchronized, and repeatable data collection system.

Key Responsibilities:

  • Integrate RGB/RGB-D cameras, depth sensors, IMUs, 6DoF trackers, and robotic/handheld grippers into data collection rigs.
  • Configure and validate multi-sensor systems for synchronized data capture.
  • Perform camera calibration, sensor calibration, and coordinate-frame alignment.
  • Configure and maintain ROS/ROS2 pipelines for sensor integration and data collection.
  • Implement and maintain data logging workflows using ROS bags, MCAP, or similar formats.
  • Develop tools and scripts in Python and/or C++ for system integration, testing, and data validation.
  • Troubleshoot hardware, software, networking, and synchronization issues across the collection setup.
  • Validate sensor data quality, timestamps, coordinate transforms, and system performance.
  • Develop repeatable data collection and validation workflows for robotics and embodied AI datasets.
  • Document system configurations, calibration procedures, troubleshooting steps, and collection protocols.
  • Collaborate with robotics, computer vision, and data collection teams to improve the reliability and scalability of the lab setup.

Required Qualifications:

  • Professional experience with robotics, computer vision, sensor integration, or related engineering fields.
  • Strong programming skills in Python and/or C++.
  • Hands-on experience with ROS or ROS2.
  • Experience working with RGB/RGB-D cameras and depth sensors.
  • Understanding of camera calibration and coordinate transformations.
  • Experience with IMUs and/or 6DoF tracking systems.
  • Comfortable working in a Linux environment.
  • Experience with Git and standard software development workflows.
  • Strong troubleshooting and problem-solving skills.
  • Ability to work hands-on with both hardware and software.
  • Basic understanding of SLAM, VIO, or related localization/visual tracking concepts.

Nice to Have:

Experience with any of the following will be considered a strong advantage:

  • OAK-D
  • Intel RealSense
  • ZED cameras
  • VIVE / SteamVR / OpenXR
  • MCAP
  • Robotics data collection platforms or datasets
  • Robotic manipulators or grippers
  • Multi-camera synchronization
  • Hand tracking or motion capture systems
  • SLAM/VIO pipelines
  • Experience with embodied AI, robot learning, or manipulation datasets

Source: TSMG careers (Lever)

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