They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D ... NeRF, Occupancy Networks). • Deep experience with point cloud and graph learning frameworks such ...
They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D ... NeRF, Occupancy Networks). • Deep experience with point cloud and graph learning frameworks such ...
3D Machine Learning Engineer
Irvine, CA · On-site
$150K - $200K/yr
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
3D Machine Learning Engineer
Irvine, CA · On-site
$150K - $200K/yr
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
3D Machine Learning Engineer
Irvine, CA · On-site
$150K - $200K/yr
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
3D Machine Learning Engineer
Irvine, CA · On-site
$150K - $200K/yr
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
Quick apply
What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... g., NeRF, Occupancy Networks). * Deep experience with point cloud and graph learning frameworks ...
Nerf Machine Learning information
What is a Nerf machine learning engineer?
Nerf Machine Learning jobs involve working with Neural Radiance Fields (NeRF), a type of machine learning model used for 3D scene reconstruction from 2D images. Professionals in this field develop, train, and optimize NeRF algorithms to create realistic 3D representations for applications in computer vision, graphics, virtual reality, and robotics. These roles typically require strong backgrounds in deep learning, computer vision, and software engineering, along with experience in frameworks like PyTorch or TensorFlow.
How does a Nerf machine learning engineer typically collaborate with 3D artists and graphics engineers in a project?
As a Nerf Machine Learning Engineer, you’ll frequently work alongside 3D artists and graphics engineers to integrate neural radiance field (NeRF) models into real-time rendering pipelines. Collaboration often involves translating real-world scene data processed by NeRF into formats that can be manipulated by artists, as well as optimizing model performance for interactive applications. Regular meetings and iterative feedback ensure that visual quality and technical requirements align, making strong communication and flexibility essential for success in this role.
What are the key skills and qualifications needed to thrive as a NeRF (Neural Radiance Fields) machine learning engineer, and why are they important?
To thrive as a NeRF Machine Learning Engineer, you need a strong background in computer vision, deep learning, and mathematics, typically supported by a degree in computer science or a related field. Proficiency with Python, PyTorch or TensorFlow, 3D graphics libraries, and familiarity with NeRF-specific frameworks is essential. Strong problem-solving skills, creativity, and effective communication set standout engineers apart in this field. These skills enable the development of advanced 3D scene reconstruction models and ensure efficient collaboration within multidisciplinary teams.
What is the difference between Nerf Machine Learning vs Computer Vision Engineer?
| Aspect | Nerf Machine Learning | Computer Vision Engineer |
|---|---|---|
| Required Credentials | Degree in Computer Science, Data Science, or related fields; experience with machine learning frameworks | Degree in Computer Science, Electrical Engineering, or related fields; experience with image processing and vision algorithms |
| Work Environment | Research labs, AI startups, tech companies focusing on neural rendering and 3D modeling | Tech companies, research institutions, industries involving image analysis and autonomous systems |
| Industry Usage | Primarily in AI research, neural rendering, 3D scene reconstruction | In autonomous vehicles, robotics, healthcare imaging, and security systems |
While both roles involve advanced AI techniques, Nerf Machine Learning focuses on neural radiance fields and 3D scene understanding, whereas Computer Vision Engineers specialize in analyzing and interpreting visual data from images and videos. The roles often overlap in AI research but serve different application areas within the tech industry.
What are popular job titles related to Nerf Machine Learning jobs in Irvine, CA?
For Nerf Machine Learning jobs in Irvine, CA, the most frequently searched job titles are:
What job categories do people searching Nerf Machine Learning jobs in Irvine, CA look for?
The top searched job categories for Nerf Machine Learning jobs in Irvine, CA are:
Job description
Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
• Experience working with BIM data, digital twins, or construction-related sensor data.
• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
• Experience working with BIM data, digital twins, or construction-related sensor data.
• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.
About Field AI
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