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Lidar Engineer information

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$46

$66

How much do lidar engineer jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for lidar engineer in Michigan is $46.74, according to ZipRecruiter salary data. Most workers in this role earn between $37.69 and $54.28 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a lidar engineer?

To thrive as a Lidar Engineer, you need a solid background in physics, mathematics, and optical engineering, as well as experience with lidar systems and data processing, often backed by a relevant engineering degree. Familiarity with lidar data analysis software (such as LAStools, ArcGIS, or CloudCompare), programming languages (like Python or C++), and certifications in remote sensing are highly advantageous. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills help set candidates apart. These skills are crucial for developing accurate lidar solutions, troubleshooting complex systems, and ensuring successful project outcomes in multidisciplinary environments.

How do you become a Lidar engineer?

To become a Lidar engineer, typically a bachelor's degree in electrical engineering, computer science, robotics, or a related field is required. Gaining experience with sensor technology, 3D mapping, and programming languages like C++ or Python is important, along with familiarity with Lidar hardware and data processing software. Advanced roles may require a master's degree or specialized certifications in related areas.

What is a lidar engineer?

A Lidar Engineer is responsible for designing, developing, and implementing LiDAR (Light Detection and Ranging) systems for applications such as autonomous vehicles, mapping, and remote sensing. They process and analyze LiDAR data to generate 3D models, improve sensor accuracy, and integrate hardware with software systems. This role requires expertise in laser scanning technology, data processing algorithms, and programming languages like Python or C++. Lidar Engineers often collaborate with robotics, geospatial, and computer vision teams to enhance perception and navigation capabilities.

What does a lidar engineer do?

As a Lidar Engineer, your daily tasks may include designing and calibrating lidar systems, analyzing point cloud data for accuracy, developing software tools for data processing, and collaborating with surveyors or GIS specialists. You might also troubleshoot hardware or software issues, document technical methodologies, and participate in field tests or deployment. Frequent collaboration with cross-functional teams—such as robotics, automotive, or remote sensing professionals—is common to integrate lidar technologies into broader applications. This hands-on, dynamic role provides exposure to both engineering challenges and real-world implementation, making each day varied and intellectually stimulating.

What are the most commonly searched types of Lidar Engineer jobs in Michigan? The most popular types of Lidar Engineer jobs in Michigan are:
What are popular job titles related to Lidar Engineer jobs in Michigan? For Lidar Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Lidar Engineer jobs in Michigan look for? The top searched job categories for Lidar Engineer jobs in Michigan are:
Lidar Engineer jobs near you
Infographic showing various Lidar Engineer job openings in Michigan as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $97,228 per year, or $46.7 per hour.

Autonomous Driving Vehicle Perception Engineer

Apetan Consulting

Northville, MI • On-site

Other

Posted 2 days ago

New

Job description

Job Title: Autonomous Driving Vehicle Perception Engineer

Location: Northville, MI (Onsite)

What You will Do:

  • Design and implement advanced perception algorithms for autonomous vehicles using LiDAR, cameras, radar, and GNSS.
  • Develop and optimize sensor fusion techniques to combine data from multiple sensors, improving the accuracy and reliability of perception systems.
  • Create algorithms for object detection, tracking, semantic segmentation, and classification from 3D point clouds (LiDAR) and camera data.
  • Work on Simultaneous Localization and Mapping (SLAM) algorithms, including Graph SLAM, LIO-SAM, and visual-inertial SLAM.
  • Develop sensor calibration techniques (intrinsic and extrinsic) and coordinate transformations between sensors.
  • Participate in real-time systems design and optimization to meet the high-performance requirements of autonomous driving.
  • Work with ROS2 for integration and deployment of perception algorithms.
  • Develop, test, and deploy machine learning models for perception tasks such as object detection and segmentation.
  • Collaborate with cross-functional teams, including software engineers, data scientists, and hardware teams, to deliver end-to-end solutions.
  • Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.

What You Will Bring:

  • Minimum 3+ years of experience in sensor calibration, multi-sensor fusion, or related domains.
  • Strong foundation in linear algebra, 3D geometry, coordinate frames, quaternions, probability, Bayesian filtering, and data association.
  • Hands-on experience with intrinsic and extrinsic calibration of LiDAR, cameras, and radar, including geometric calibration, coordinate transforms, and sensor synchronization.
  • Proven experience with perception algorithms for autonomous systems, particularly in the areas of LiDAR, camera, radar, GNSS, or other sensor modalities.
  • Deep understanding of LiDAR technology, point cloud data structures, and processing techniques; experience with PCL or Open3D.
  • Proficiency in sensor fusion for combining data from LiDAR, camera, radar, and GNSS, including handling time synchronization and motion distortion.
  • Solid background in computer vision techniques; experience with OpenCV and object detection models such as YOLO, Faster R-CNN, or SSD.
  • Experience with deep learning frameworks (TensorFlow or PyTorch) for object detection and segmentation tasks.
  • Hands-on experience with multi-object tracking algorithms such as SORT, DeepSORT, Kalman Filters, UKF, IMM, or JPDA.
  • Strong programming skills in C++ and Python; familiarity with geometric optimization libraries.
  • Familiarity with ROS2 for perception-based autonomous systems development.
  • Experience with parallel computing for real-time performance optimization (e.g., CUDA, OpenCL).

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