Manufacturing Data Engineer for CAD & AI Training

Remote; San Francisco (CA); Hybrid; USD 120,000 - 190,000; Full time

Job summary

Logoslabs in San Francisco is seeking an Individual Contributor for the Data Studio team. You will transform raw customer data into structured, high-fidelity datasets powering AI model training, evaluation, and delivery.

The role combines product, research, and engineering, requiring hands-on mechanical engineering and manufacturing design experience to craft data pipelines, labeling workflows, and reference models.

Qualifications

• Strong domain expertise in mechanical engineering, manufacturing design, or industrial workflows.
• Hands-on experience with CAD tools (SolidWorks, CATIA, Siemens NX, Creo).
• Familiarity with annotation tools and illustration software (Creo Illustrate, Adobe Illustrator, Arbortext).
• Ability to interpret complex mechanical assemblies, drawings, GD&T, and engineering documentation.
• Experience creating exploded views, work-step sequences, or manufacturing instructions.
• Strong problem-solving and cross-functional collaboration skills.

Responsibilities

• Ingest, clean, transform, and structure customer and internal data for AI training and inference.
• Design and build mechanical components and assemblies in CAD to serve as ground truth for evaluation.
• Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other artifacts encoding engineering reasoning.
• Define and assess output quality across datasets using engineering judgment.
• Collaborate with Product Managers to shape tooling for annotation, data correction, and pipeline automation.
• Provide feedback on tool usability and automate opportunities to improve throughput.

Job description

Logoslabs in San Francisco is seeking an Individual Contributor for the Data Studio team. You will transform raw customer data into structured, high-fidelity datasets powering AI model training, evaluation, and delivery.

The role combines product, research, and engineering, requiring hands-on mechanical engineering and manufacturing design experience to craft data pipelines, labeling workflows, and reference models.