About the role
3D Machine Learning Engineer at FieldAI in Irvine, CA. On-site, paying $150k–$200k. Posted July 17, 2025.
What You’ll Do
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Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
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Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
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Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
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Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
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Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
What You Have
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Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
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2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
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Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
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Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
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Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
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Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
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Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
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Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
The Extras That Set You Apart
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Experience working with BIM data, digital twins, or construction-related sensor data.
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Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
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Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
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Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
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Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
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Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
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Experience building custom modules for SparseConvNet or 3D transformers.
Prefer to apply yourself? Apply on FieldAI’s site
Quick answers
How much does this FieldAI job pay?
The posted pay is $150k–$200k.
Where is this job based?
Irvine, CA. The role is on-site.
How much experience do you need?
The posting asks for 2+ years.
How do you apply?
Apply on FieldAI’s site using the link on this page. With a free Jobmagic account you can see how well you fit first.
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