Mechanical Data Engineer (Mechanical + Data Engineering Required)

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 Responsibilities1. Data Creation, Processing & QualityIngest, clean, transform, and structure customer and internally generated engineering data for AI training and inferenceDesign and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systemsProduce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoningApply engineering judgment to define and assess output quality across datasetsContinuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets2. Workflow & Tooling ContributionsCollaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automationProvide detailed feedback on tool usability, workflow efficiency, and automation opportunitiesHelp develop scalable, repeatable data processes that improve throughput and data consistency3. Cross-Functional CollaborationPartner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new dataInfluence model behavior by supplying representative engineering examples and ground-truth mechanical designsPartner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specificationsServe as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts4. Domain Expertise & Reference Content CreationGenerate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training dataEnsure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practicesEmbed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent5. Customer & Project SupportWork with customers to understand their data sources, schemas, formats, and quality expectationsGuide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelinesSupport delivery timelines by communicating progress clearly and surfacing risks or issues earlyReview and work with external contractors, ensuring high-quality output and adherence to SOPsRequired QualificationsStrong domain expertise in mechanical engineering, manufacturing design, or industrial workflowsHands-on experience with CAD tools such as SolidWorks, CATIA, Siemens NX, or CreoFamiliarity 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 documentationExperience creating artifacts like exploded views, work-step sequences, repair manuals, or manufacturing instructionsStrong problem-solving skills and the ability to translate domain workflows into structured data requirementsExcellent communication and cross-functional collaboration skillsPreferred QualificationsExperience 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 environmentsComfort with customer-facing discovery or solutioningWhat Success Looks LikeDeliver high-quality datasets that measurably improve model performanceDrive standardization and reliability across ME datasets, CAD models, workflows, metadata, and annotationsEnable faster model training, evaluation, and deployment through strong cross-functional collaborationMaintain clear documentation, repeatable processes, and continuous quality improvementBe recognized as a trusted ME expert in data quality and domain insightWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.