Data Studio Engineer

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

What Success Looks Like

• 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.