Foundation EGI - Mechanical Data Engineer- (Mechanical Data Exp Required)

• 1. 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. • 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. • 2. 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. • Help develop scalable, repeatable data processes that improve throughput and data consistency. • 3. Cross-Functional Collaboration • 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. • 4. Domain Expertise & Reference Content Creation • 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. • 5. Customer & Project Support • 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. • Strong domain expertise in mechanical engineering, manufacturing design, or industrial workflows. • Hands-on experience with CAD tools such as SolidWorks, CATIA, Siemens NX, or Creo. • Familiarity with annotation tools and illustration software (e.g., Creo Illustrate, Adobe Illustrator, Arbortext). • Ability to interpret complex mechanical assemblies, technical drawings, GD&T, and engineering documentation. • Experience creating artifacts like exploded views, work-step sequences, repair manuals, or manufacturing instructions. • Strong problem-solving skills and the ability to translate domain workflows into structured data requirements. • Excellent communication and cross-functional collaboration skills. • Experience with data operations, labeling workflows, ML data pipelines, or AI/ML data lifecycle (collection -> labeling -> QA -> training -> evaluation -> deployment). • Experience in fast-paced startup or high-growth environments. • Comfort with customer-facing discovery or solutioning. • Deliver high-quality datasets that measurably improve model performance. • Drive standardization and reliability across ME datasets, CAD models, workflows, metadata, and annotations. • Enable faster model training, evaluation, and deployment through strong cross-functional collaboration. • Maintain clear documentation, repeatable processes, and continuous quality improvement. • Be recognized as a trusted ME expert in data quality and domain insight.