Mechanical Data Engineer

Data Creation, Processing & Quality:

• Ingest, clean, transform, and structure customer and internally generated engineering data for AI training and inference.
• Design and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systems.
• Apply engineering judgment to define and assess output quality across datasets and maintain a living definition of quality.

Workflow & Tooling Contributions:

• Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation.
• Provide detailed feedback on tool usability, workflow efficiency, and automation opportunities to develop scalable, repeatable data processes.

Cross-Functional Collaboration & Domain Expertise:

• Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data.
• Generate technical documentation, exploded views, and annotations that encode engineering reasoning into training data, ensuring datasets reflect real-world constraints.
• Work with customers to understand their data sources, schemas, formats, and quality expectations, guiding them in preparing high-quality datasets.