Data Studio Engineer - Mechanical Engineering

We are an MIT-born, venture-backed Silicon Valley startup building Engineering General Intelligence (EGI)—an AI Copilot for design and manufacturing. Our mission is to fundamentally reinvent how physical products are designed and built, dramatically accelerating the pace of product development.

As an Individual Contributor on the Data Studio team, you will play a key role in transforming raw customer data into structured, high-fidelity datasets that power model training, evaluation, and customer delivery. This role is deeply hands-on and sits at the intersection of product, research, and engineering. You will apply your mechanical engineering and manufacturing expertise to create data pipelines, labeling workflows, reference models, and quality checks that ensure the accuracy and reliability of our AI systems. Mechanical engineering or manufacturing design experience is essential; candidates without this background will not be considered.

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.

• 1. 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.

• 1. 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.

• 1. 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.

• 1. 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.