3D Machine Learning Engineer

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

Field AI is at the forefront of robotic embodied AI, transforming industries like construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact.

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As a 3D Machine Learning Engineer
, you will focus on designing, implementing, training, and maintaining cutting-edge 3D and multimodal machine learning models that process reality capture data such as 3D point clouds, 360 photos, and RGBD images. Your work will directly contribute to automated progress tracking, deviation analysis, and semantic scene understanding of construction sites. You will collaborate closely with software, autonomy, and product teams to ensure seamless integration of these AI models into our production environments.

• 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, Tensor Flow, or equivalent frameworks on cloud platforms such as AWS (e.g., Sage Maker, 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.

• 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, Tensor Flow, 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, Sage Maker) 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.

• 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, Sage Maker, 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 Sparse Conv Net  or 3D transformers.

Our salary range is between ($70,000 - $300,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay…