Note: The job is a remote job and is open to candidates in USA. Foundation EGI is an MIT-born, venture-backed startup building Engineering General Intelligence, an AI copilot for design and manufacturing. The Data Studio Engineer will transform customer and internal engineering data into high-fidelity datasets, reference models, labeling workflows, and quality checks for AI model training, evaluation, and delivery. The role applies mechanical engineering and manufacturing expertise while collaborating with product, research, engineering, and customer-facing teams.
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
- 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
Skills
- 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
Benefits
- Remote work arrangement
Company Overview
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