AI Machine Learning Engineer 3D Developer (remote part/full time)

Role overview This role sits at the intersection of deep learning, 3D geometry, and industrial automation, focusing on building AI systems that interpret engineering CAD data to power quoting, calculation, and optimization tools for manufacturing. The work spans computer vision, cloud services, and edge integration, translating model outputs into tangible outcomes on real factory floors rather than purely screen-based products. It combines research-style prototyping with end-to-end ownership of production pipelines that operate under continuous load.

Responsibilities - Design and ship feature-extraction pipelines for 3D CAD, 2D drawings, and batch datasets that feed supervised and unsupervised learning systems. - Build, train, and industrialize deep learning models focused on geometric understanding, then harden them for production workloads. - Extend internal tooling for parsing, analyzing, and rendering 2D and 3D geometry in support of manufacturing workflows. - Architect and own cloud services, APIs, and automated model-training and deployment pipelines from prototype through production. - Work across interconnected cloud, data, computer vision, and ML services, integrating components written in multiple languages and platforms.

Requirements - Hands-on machine learning or deep learning experience building, training, and shipping models in frameworks such as PyTorch, PyTorch Lightning, or scikit-learn, well beyond coursework or library calls. - Strong mathematical foundation in linear algebra, geometry, statistics, and probability, since the underlying problems are mathematical in nature. - Five or more years of professional software engineering across both dynamically and statically typed languages (for example Python plus C++ or C#), with strong problem-solving skills. - Substantial hands-on background in game development, 3D geometry, rendering, fluid simulation, or CUDA and GPU programming. - Track record of operating or maintaining systems under heavy production load.

Nice to have - CAD system development experience or exposure to CNC and broader mechanical manufacturing industries. - Familiarity with ML industrialization tooling such as ONNX, quantization, Docker, and MLOps practices for automated training and deployment. - Experience working with large datasets: processing, clustering, filtering, indexing, querying, statistical testing, and outlier analysis. - Cloud services experience on AWS or Azure, SQL and NoSQL databases, and full-stack skills such as Angular.