3D Machine Learning Engineer

About Field AI

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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About the Role

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.

What You’ll Do

• 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

What You Have

• 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

The Extras That Set You Apart

• 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

Compensation and Benefits

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 offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option.

Why Join Field AI?

We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.

You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.

Be Part of the Next Robotics Revolution

To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.

We are headquartered in always-sunny Irvine, Southern California and have US based and global teammates.

Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!

We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.