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3D Lidar Segmentation Jobs (NOW HIRING)

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 · On-site

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

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

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

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, and why are they important?

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 July 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution.

Machine Learning Engineer: Perception

Bedrock Robotics

San Francisco, CA • On-site

Full-time

Re-posted 3 days ago

Job description

Join the team bringing advanced autonomy to the built world
At Bedrock, we're moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we're working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage.
This is where algorithms meet steel-toed boots. You'll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team-we'd love to have you join us.
Machine Learning Engineer: Perception
Bedrock is bringing autonomy to the construction industry! We're a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market.
We are looking for engineers with expertise in shipping production 3D perception systems at scale. Successful candidates have architected systems, trained models from scratch, understand the full stack (clustering, detection, classification, and tracking), and have shipped at scale. We use both computer vision and LIDAR-based approaches, so knowledge of either or both is key. Models are just part of the system: you understand data and have good intuition about why models fail. You know how to evaluate corner cases, manage or build data pipelines, use autolabels (or not), and have a strong understanding of statistical properties of these systems.
What You'll Do:
  • Design Early Fusion Architectures: Develop and train state-of-the-art models (e.g., BEV-based transformers) that fuse raw Lidar and Camera data to solve for object detection and semantic segmentation.
  • Tackle "Messy" Physics: Build perception systems robust enough to handle dynamic occlusion (seeing the robot's own arm/bucket), particulates (dust, snow, rain), and high-vibration conditions.
  • Deploy to the Edge: Optimize models for inference on embedded hardware. You will debug system-level issues, such as sensor calibration drift and latency bottlenecks.
  • Collaborating with other teams to create state-of-the-art representations for downstream use cases.

What we're looking for:
  • Production ML Experience: 3+ years of experience taking deep learning models from research to real-world production using PyTorch, Tensorflow, or JAX.
  • 3D Geometry & Calibration: You have a deep understanding of SE(3) transformations, homogeneous coordinates, and intrinsic/extrinsic sensor calibration. You understand the math required to project a 3D Lidar point onto a 2D image pixel accurately.
  • Early Fusion Expertise: Practical experience with architectures that fuse modalities at the feature level (e.g., BEVFusion, TransFuser, PointPainting) rather than just fusing final bounding boxes.
  • SOTA Object Detection experience with modern transformer-based architectures (DETR, PETR, etc...) including similar temporal models (PETRv2, StreamPETR, ...)
  • Systems Fluency: You are an expert in Python, but you are also comfortable reading and writing systems code in C++ or Rust. You understand memory management and real-time constraints.
  • Data Intuition: You understand that in robotics, better data alignment often beats a bigger model. You are willing to dig into the data infrastructure to ensure ground truth quality.

Ways to stand out:
  • Bonus: Voxel/Occupancy Experience: Experience working with occupancy grids, NeRFs, or voxel-based representations for terrain mapping.
  • Bonus: Top-Tier Research: Published work in conferences such as ICRA, IROS, CVPR, ECCV, ICCV, CoRL, or RSS

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

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