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Hourly Lidar Data Processing Jobs in Michigan (NOW HIRING)

Senstar

Field Operations Technician

Adrian, MI · On-site

In this role, you'll support the installation and deployment of LiDAR-based systems, gaining real ... Contribute ideas to improve field processes and deployment efficiency What You Bring... * 0-3 years ...

Judge Group, Inc.

Data Scientist

Sterling Heights, MI · On-site

$50 - $58/hr

Sterling Heights, MI Salary: $50.00 USD Hourly - $58.00 USD Hourly Description: Our client is ... Be directly involved in developing data models and process flows as well as liaising with multiple ...

Senstar

Field Operations Technician

Adrian, MI · On-site

In this role, you'll support the installation and deployment of LiDAR-based systems, gaining real ... Contribute ideas to improve field processes and deployment efficiency What You Bring... * 0-3 years ...

Georgetown University

Data Analyst

Detroit, MI · On-site

$75.25/hr

Develops an understanding of research processes * Documents business requirements of research ... The projected salary or hourly pay range for this position which represents the full range of ...

New

Skill Corp

Pharmacy Technician

Canton, MI · On-site

$16 - $18/hr

Temporary Salary: $16-18 Hourly Start Date: Aug 14, 2026 Schedule: Monday- Friday 11am to 8pm ... Enter member demographics and prescription information into the data processing system. * Research ...

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Hourly Lidar Data Processing information

What is hourly LiDAR data processing?

Hourly LiDAR data processing refers to the analysis and interpretation of LiDAR (Light Detection and Ranging) data on an hourly basis. This involves collecting raw LiDAR data, cleaning and filtering the data, and generating useful outputs such as 3D models or elevation maps. The role is important in industries like surveying, forestry, construction, and autonomous vehicles, where up-to-date spatial information is crucial for decision-making. Professionals in this field use specialized software to process and manage large datasets efficiently. The hourly aspect usually means that workers are paid by the hour and may handle tasks as projects or data come in.

What are some common challenges faced in hourly LiDAR data processing roles and how can they be addressed?

Hourly Lidar data processing professionals often encounter challenges such as managing large volumes of raw data, ensuring data quality and accuracy, and meeting tight turnaround times. Dealing with inconsistencies in data due to varying environmental conditions or equipment calibration can also be demanding. To address these, it’s helpful to follow standardized workflows, use automated tools for data cleaning and classification, and maintain clear communication with the data collection team. Staying organized and collaborating closely with colleagues can streamline processing and help ensure deliverables meet client expectations.

What are the key skills and qualifications needed to thrive as an hourly LiDAR data processing specialist, and why are they important?

To thrive in Hourly Lidar Data Processing, you need a solid understanding of geospatial data concepts, attention to detail, and experience with Lidar data formats, often supported by coursework or training in geography, GIS, or remote sensing. Proficiency in specialized software such as LAStools, ArcGIS, QGIS, and familiarity with point cloud processing systems is typically required. Analytical thinking, problem-solving, and strong organizational skills help individuals stand out in managing large datasets efficiently. These skills are crucial for ensuring accurate, timely, and reliable data outputs that support mapping, surveying, and environmental analysis projects.

What is the difference between Hourly Lidar Data Processing vs Lidar Data Analyst?

AspectHourly Lidar Data ProcessingLidar Data Analyst
Primary RoleProcessing raw Lidar data into usable formatsAnalyzing processed Lidar data for insights
Skills & CertificationsGIS, remote sensing, data processing toolsGIS, data analysis, reporting skills
Work EnvironmentField data collection, office processingOffice-based data analysis and reporting
Industry UsageSurveying, mapping, environmental studiesUrban planning, infrastructure, research

Hourly Lidar Data Processing focuses on converting raw Lidar data into usable formats, requiring technical skills in data processing. Lidar Data Analysts interpret and analyze this processed data to generate insights. While both roles require knowledge of GIS and remote sensing, processing is more technical and hands-on, whereas analysis emphasizes interpretation and reporting.

What are the most commonly searched types of Lidar Data Processing jobs in Michigan?

The most popular types of Lidar Data Processing jobs in Michigan are:

What cities in Michigan are hiring for Hourly Lidar Data Processing jobs?

Cities in Michigan with the most Hourly Lidar Data Processing job openings:

Hourly Lidar Data Processing jobs near you

Autonomous Driving Vehicle Perception Engineer

Reveille Technologies

Northville, MI • On-site

Other

Posted 14 days ago

Job description

ONLY FULLTIME NO CONTRACT

Job Title: Autonomous Driving Vehicle Perception Engineer

Location: Northville, MI (Onsite)

Type: Full-time

About the Role

We are seeking an experienced Perception Engineer to design, build, and deploy real-time perception and multi-sensor fusion algorithms for next-generation autonomous driving systems across LiDAR, camera, radar, and GNSS modalities.

Key Responsibilities

  • Algorithms & Models: 3D Object Detection, Multi-Object Tracking, Semantic Segmentation, Machine Learning (PyTorch, TensorFlow, YOLO, Faster R-CNN, DeepSORT).

  • Localization & SLAM: Graph SLAM, LIO-SAM, Visual-Inertial SLAM, Point Cloud Processing (PCL, Open3D).

  • Sensor Fusion & Calibration: Intrinsic & Extrinsic Calibration, Multi-Sensor Fusion (LiDAR, Camera, Radar, GNSS), Coordinate Transformations, Time Synchronization.

  • System Integration & Optimization: ROS2, C++, Python, Real-Time Systems, Parallel Computing (CUDA, OpenCL).

Key Requirements

  • Experience: 3+ years in sensor calibration, multi-sensor fusion, or autonomous vehicle perception.

  • Core Fundamentals: Strong background in 3D geometry, coordinate frames, quaternions, probability, Bayesian filtering, and data association.

  • Tech Stack: High proficiency in C++ and Python; hands-on experience with ROS2 and computer vision libraries (OpenCV, PCL, or Open3D).

  • Deep Learning & Tracking: Experience with PyTorch/TensorFlow, object detection models (YOLO, Faster R-CNN), and tracking algorithms (Kalman Filters, DeepSORT, UKF).

  • Optimization: Familiarity with parallel computing platforms (CUDA/OpenCL) for real-time performance.

Thanks and Regards,

Praveenkumar

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