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3D Lidar Segmentation Jobs (NOW HIRING)

Apptronik

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

You will also integrate data from multiple modalities-Cameras, LiDAR, depth sensors, and IMUs-into ... segmentation, multi-object tracking, and 3D perception. * Hands-on experience with modern AI ...

Apptronik

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $167K/yr

... segmentation, multi-object tracking, and 3D perception. • Hands-on experience with modern AI ... LiDAR, depth sensors, and IMUs for robust real-time perception in dynamic human-centered ...

Kinetic

Senior Machine Learning Engineer

Costa Mesa, CA · On-site

$112K - $154K/yr

... mapping, lidar, motorsport, and ride-sharing. We are a venture backed startup (Series B) with a ... instance segmentation and object/damage detection across 2D and 3D data. Experience & Skills ...

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3D Lidar Segmentation information

What is 3D LiDAR segmentation?

3D Lidar segmentation is the process of dividing or clustering raw Lidar point cloud data into meaningful parts or objects, such as vehicles, pedestrians, buildings, or vegetation. This technique is crucial for applications like autonomous driving, mapping, and robotics, where understanding the environment in three dimensions is essential. By segmenting the data, algorithms can better identify and track objects, enabling safer navigation and more detailed scene analysis.

What are the key skills and qualifications needed to thrive as a 3D LiDAR segmentation specialist?

To thrive as a 3D Lidar Segmentation Specialist, you need a strong background in computer vision, machine learning, and point cloud data processing, often supported by a degree in computer science, engineering, or related fields. Familiarity with tools such as Python, C++, ROS, and libraries like PCL and Open3D, as well as experience with deep learning frameworks (e.g., TensorFlow, PyTorch), is essential. Analytical thinking, attention to detail, and effective problem-solving are crucial soft skills for interpreting complex data and collaborating in multidisciplinary teams. These competencies ensure accurate scene understanding, efficient workflow, and the development of robust solutions for applications like autonomous vehicles and robotics.

What are some common challenges faced by professionals working in 3D LiDAR segmentation, and how are they typically addressed?

Professionals in 3D LiDAR segmentation often encounter challenges such as dealing with noisy or incomplete data, managing large-scale datasets, and ensuring accurate object classification in complex environments. These challenges are commonly addressed through advanced preprocessing techniques, robust machine learning algorithms, and leveraging high-performance computing resources. Collaboration with data engineers, software developers, and domain experts is also essential to refine segmentation models and improve overall system performance.
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3D Lidar Segmentation jobs near you
Infographic showing various 3D Lidar Segmentation job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 88% Full Time, 8% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

Senior Edge AI Perception Engineer

AION ROBOTICS CORPORATION

Arvada, CO • On-site

$107K - $147K/yr

Full-time

Re-posted 7 days ago

Job description

Job Summary:
AION Robotics Corporation is a rapidly growing startup manufacturing advanced autonomous ground vehicles for critical infrastructure monitoring. They are seeking a highly skilled Senior Edge AI Perception Engineer to design, optimize, and deploy deep learning models for real-time autonomous vehicle perception systems.
Responsibilities:
• Neural Network Model Development & Optimization
• Build and manage optimized neural network pipelines tailored for edge deployment in autonomous vehicle systems.
• Implement, compress, and optimize models (pruning, quantization, scheduling) to run on GPU, DLA, and Tensor cores.
• Work with architectures including monocular depth models, YoloX, PeopleNet, ResNet, and others.
• Leverage NVIDIA DeepStream, TensorRT, CUDA, and TAO Toolkit to create high-performance perception pipelines.
• Manage model/hardware resource allocation across GPU/DLA for real-time scheduling and execution.
• Optimize pipelines “lens-to-detections” meeting ultra-low latency constraints on embedded devices such as NVIDIA Orin & Thor.
• Apply real-time geometric transforms to object detections and semantic segmentation results for geo-referencing and LiDAR point cloud filtering.
• CUDA accelerated 3D Terrain mapping
• Develop and maintain automated data collection pipelines, including dataset formatting, labeling workflows (CVAT), and fine-tuning for custom training.
• Implement real-time dewarped camera pipelines using GMSL drivers, VIC, and ARGUS APIs on embedded platforms.
• Collaborate with hardware engineers to achieve consistent calibration and synchronization across multiple sensors.
• Stay up to date with bleeding-edge advancements in neural networks, edge AI optimization, and autonomous perception.
• Rapidly prototype and validate new models and methods for production deployment.
Qualifications:
Required:
• Direct real-world experience in computer vision, deep learning, or edge AI systems.
• Strong proficiency with CUDA, TensorRT, NVIDIA DeepStream, TAO Toolkit, PyTorch/TensorFlow.
• Demonstrated experience with model optimization techniques (e.g., pruning, quantization, distillation, scheduling).
• Hands-on experience deploying AI pipelines to embedded edge platforms (preferably NVIDIA Jetson Orin or Thor).
• Expertise in object detection, semantic segmentation, and depth estimation.
• Solid understanding of real-time embedded system constraints and low-latency optimization strategies.
• Strong programming skills in C++ and Python.
Preferred:
• Experience with low-level camera system integration, including ISP tuning, intrinsic & extrinsic calibration algorithms, multi-camera synchronization and fusion.
• Familiarity with sensor fusion pipelines combining camera, LiDAR, and IMU.
• Direct experience working in autonomous vehicles, robotics, ROS2 or safety-critical perception systems.
Company:
Our rugged autonomous vehicles bring Industry 4.0 to outdoor commercial jobsites through the automation of infrastructure monitoring, maintenance and inspection tasks. Founded in 2016, the company is headquartered in Denver, USA, with a team of 11-50 employees. The company is currently Early Stage.

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