Data Engineer

• 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.
• Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoning.
• Apply engineering judgment to define and assess output quality across datasets.
• Continuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets.
• 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.
• Help develop scalable, repeatable data processes that improve throughput and data consistency.
• Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data.
• Influence model behavior by supplying representative engineering examples and ground-truth mechanical designs.
• Partner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specifications.
• Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts.
• Generate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training data.
• Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices.
• Embed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent.
• Work with customers to understand their data sources, schemas, formats, and quality expectations.
• Guide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelines.
• Support delivery timelines by communicating progress clearly and surfacing risks or issues early.
• Review and work with external contractors, ensuring high-quality output and adherence to SOPs.

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