{"id":624384,"url":"https://alion.io/job/fieldai-3d-machine-learning-engineer","title":"3D Machine Learning Engineer","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":106,"ghost_share":0.217,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":65,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United 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Library","optional":false},{"name":"PyG","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2025-07-17T04:01:03Z","employer_posted_date":"2025-07-17","last_verified_at":"2026-10-29T01:44:44Z","board_verified":true,"closed_at":null,"days_open":468,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":468},"description":"What You’ll Do\nDesign and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.\nTrain, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).\nCollaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.\nAnalyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).\nWork closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.\nWhat You Have\nBachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.\n2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.\nStrong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.\nStrong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.\nFamiliarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.\nExperience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.\nSolid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.\nProven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.\nThe Extras That Set You Apart\nExperience working with BIM data, digital twins, or construction-related sensor data.\nBackground in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.\nFamiliar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.\nStrong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.\nExposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).\nDeep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.\nExperience building custom modules for 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