Sr. Machine Learning Engineer - 3D Modeling

Role overview Own machine learning work from experimentation and model development through deployment and production monitoring. The work spans deep learning, signal processing, and LLM-powered features, with a particular focus on turning images into clean, printable 3D models.

Responsibilities - Design, build, and deploy ML models and AI features for production systems. - Develop image-to-3D pipelines that cover generation, segmentation, mesh repair, and slicer preparation. - Maintain the full ML workflow, from data ingestion and feature engineering through training, evaluation, and serving. - Apply LLM techniques such as prompt engineering, retrieval-augmented generation, observability, and orchestration. - Work with backend and data engineering colleagues to integrate ML into existing systems. - Monitor models in production and support experiment tracking, versioning, CI/CD, and ML observability.

Requirements - Three or more years of professional experience in machine learning or AI engineering. - Strong knowledge of supervised and unsupervised learning, feature engineering, and model evaluation. - Hands-on experience building and shipping production ML systems. - Proficiency in Python and at least one deep learning framework; PyTorch is preferred. - Experience with APIs, practical LLM applications, cloud ML tooling, and Docker. - Familiarity with software engineering practices such as testing, version control, and CI/CD. - Advanced written and spoken English for stakeholder communication.

Nice to have - Background in 3D geometry, meshes, point clouds, image-to-3D generation, mesh repair, 3D printing slicers, or CAD. - Computer vision and generative model experience, including running GPU workloads and evaluating results. - Familiarity with responsible AI and model evaluation frameworks, or an interest in current ML research.

Benefits and work setup - Hybrid work in Montevideo or remote work within Latin America.