Data Studio Lead - Mechanical Engineering

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

The Data Studio Lead owns the end-to-end process of transforming raw customer data into usable, high quality datasets and insights that power development, evaluation, and delivery. This role sits at the intersection of product, research, engineering, and business teams. You will build and lead a team responsible for data pipelines, labeling workflows, quality assurance, and customer-specific data solutions that either enable our developers and researchers to advance the product or deliver the solution directly to customers.

Mechanical engineering or manufacturing design experience is essential; candidates without this background will not be considered.

Key Responsibilities

• Data Operations & Delivery:

• Own the definition and evaluation of output quality, ensuring consistency and accountability across all internal and external datasets.

• Oversee ingestion, cleaning, transformation, and structuring of customer data for AI model training and inference.

• Build scalable processes and frameworks for data labeling, annotation tools, quality checks, and feedback loops.

• Ensure timely, high quality data delivery for internal R&D and customer deployments.

• Own SLAs, accuracy targets, and "definition of quality" for all datasets.

• Product & Solution Leadership:

• Partner with Product Managers to shape data-related features, workflows, and tooling (e.g., annotation UI, correction UI, model output review tools).

• Translate customer needs into clear data requirements and repeatable solutions.

• Team & Process Leadership:

• Build and lead a Data Studio team.

• Implement best practices for documentation, quality metrics, repeatable workflows, and compliance.

• Drive continuous improvements in throughput, cost efficiency, and accuracy.

• Cross-Functional Collaboration:

• Work closely with engineering and research teams to define data needs for model training, evaluation, and debugging.

• Collaborate with BD team to scope data requirements for onboarding and implementation.

• Serve as the subject matter expert on data quality, data formats, labeling standards, and customer specific nuances related to manufacturing design.

• Customer & Project Management:

• Interface with customers to understand their domain, data structures, and quality expectations.

• Guide customers through data preparation, schema definition, and ongoing iteration.

• Own delivery timelines, reporting, and communication for all active data projects.

Required Qualifications

• Domain expertise in manufacturing design or industrial workflows; mechanical engineering background is required.

• Hands-on experience with CAD tools (e.g., SolidWorks, CATIA, Siemens NX, Creo) and familiarity with interpreting complex mechanical models.

• Experience creating or working with technical documentation such as exploded views, work-step sequencing, GD&T drawings, repair manuals, and similar manufacturing/assembly artifacts.

• Ability to work directly with customers and translate domain problems into data requirements.

• Experience leading and growing teams.

• Excellent project management and cross-functional communication skills.

Preferred Qualifications

• Familiarity with annotation tools and Illustration tools (e.g., Creo Illustrate, Adobe Illustrator, Arbortext)

• Experience in data operations, data analytics, ML operations, or related fields.

• Strong understanding of AI/ML data lifecycle: collection -> labeling -> QA -> training -> evaluation -> deployment.

• Ability to partner with product managers to shape data tooling and internal UIs.

• Experience in a startup or high-growth tech environment.

What Success Looks Like

• High-quality, on-time data delivery for every customer and internal teams.

• Increasing automation and decreasing manual effort in the data pipeline.

• Clear standards for data quality that align with model performance needs.

• Data Studio becomes a strategic partner for customers in automating their workflows.