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Mid LevelFull-timeRustAI
About the Role
We are seeking an Individual Contributor for the Data Studio team to transform raw customer data into structured, high-fidelity datasets for AI model training and evaluation. This hands-on role sits at the intersection of product, research, and engineering, requiring mechanical engineering and manufacturing expertise to ensure data accuracy and reliability.
Key Responsibilities
- →Ingest, clean, transform, and structure engineering data for AI training and inference
- →Design and build high-quality mechanical components and assemblies in CAD as ground truth for AI systems
- →Produce labeled datasets, reference designs, annotations, exploded views, and other engineering artifacts
- →Apply engineering judgment to define and assess output quality across datasets
- →Collaborate with Product Managers to shape annotation, data correction, and pipeline automation tooling
- →Partner with engineering and research teams to understand model data requirements and failure modes
- →Generate technical documentation, exploded views, and annotations that encode engineering reasoning into training data
- →Work with customers to understand data sources, schemas, formats, and quality expectations
- →Support delivery timelines by communicating progress and surfacing risks 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 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 (preferred)
- ✓Experience in fast-paced startup or high-growth environments (preferred)
- ✓Comfort with customer-facing discovery or solutioning (preferred)
