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3D Lidar Segmentation Jobs (NOW HIRING)

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

... Annotate and segment mining site entities (haul roads, berms, rock piles, vehicles, personnel, machinery) in 2D video and 3D LiDAR/radar data. - Track and map heavy equipment trajectories ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

... 3D spatial data LiDAR labelling platforms Job Duties - Key Responsibilities 1. Mining Perception & Spatial Sensor Annotation 3D Spatial Bounding & Segmentation: Annotate site entities (haul roads ...

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

... segmentation, and scene understanding. • Train, optimize, and deploy deep learning models using ... imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM). • Work ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ... segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ... segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ... segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work ...

Mach9 Robotics Inc

ML Engineer

San Francisco, CA · On-site

$180K - $300K/yr

We build models to extract 3D object and line features from dense LiDAR point clouds and imagery ... segmentation, detection, or 3D understanding. * Experience taking a ML model from research ...

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3D Lidar Segmentation information

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.

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 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.
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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 92% Full Time, and 8% Contract. Highlights an 77% In-person, and 23% Remote job distribution.

Data Annotator

Expert Technology Services

Irving, TX • On-site

$109K - $132K/yr

Contractor

Posted 7 days ago

Job description

Job Summary (List Format): Data Annotator & QA Reviewer – Autonomy & Robotics (Mining)
Main Responsibilities:
- Execute manual data annotation and rigorous QA review for perception (video, images, sensor data) and VLA (Vision-Language-Action) models.
- Annotate and segment mining site entities (haul roads, berms, rock piles, vehicles, personnel, machinery) in 2D video and 3D LiDAR/radar data.
- Track and map heavy equipment trajectories, articulation angles, and operational states in complex mining environments.
- Align and cross-reference visual data with multi-sensor telemetry (LiDAR, radar, GPS/GNSS, IMU, CAN bus, payload sensors).
- Decompose mining tasks into granular actions, label machine/operator intent, and model chains of causation for operational behaviors.
- Tag and verify outcomes, comparing expected vs. actual results (e.g., full bucket loads, tire slips, dumping success).
- Conduct thorough QA audits to ensure high precision, especially in mining-specific edge cases (dust, mud, poor lighting, complex terrain).
- Audit temporal consistency and semantic accuracy in annotated datasets.
- Provide structured feedback and help update annotation guidelines as new mining scenarios emerge.
Key Skills & Requirements:
- 1+ years of professional experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Experience with 3D spatial data (LiDAR, depth maps, multi-camera feeds).
- Proficient in standard labeling platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.).
- Ability to systematically break down and label complex heavy machinery actions and interactions.
- Strong 3D spatial visualization and attention to detail, especially for mining environments and hazards.
- Working knowledge of mining operations, heavy equipment mechanics, and pit safety terminology.
- Comfortable with geospatial formats, sensor logs, and structured metadata (JSON/XML).
Preferred/Bonus Qualifications:
- Background in Mining Engineering, Geotechnical Engineering, Robotics, Autonomous Vehicles, or related fields.
- Experience with autonomous haulage systems (AHS), telemetry logs, or VLA models for industrial robotics.
Soft Skills:
- High attention to detail and precision in annotation.
- Effective communication and feedback skills for collaboration and guideline improvement.
- Ability to handle complex, ambiguous scenarios in high-stakes autonomous environments.