Data Engineer - Manufacturing

About This Role

This role involves transforming raw customer data into structured datasets to power AI model training and evaluation, applying mechanical engineering and manufacturing expertise to build data pipelines and ensure data accuracy. The Data Engineer will collaborate across product, research, and engineering teams to accelerate product development in the design and manufacturing space.

Responsibilities

• 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.
• Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation.
• Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data.
• Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts.
• Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices.
• Work with customers to understand their data sources, schemas, formats, and quality expectations.
• Support delivery timelines by communicating progress clearly and surfacing risks or issues early.

Requirements

• 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.

Nice to Have

• 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.

Skills

* Required skills