Mechanical Data Engineer – Mechanical, Data Engineering

🕒 vor 2 Monaten

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

🚰 Dateningenieur

Jetzt Bewerben

Ähnliche Remote-Jobs finden

E-Mail-Benachrichtigung für neue Jobs einrichten

📊 Überprüfen Sie Ihre Lebenslauf-Bewertung für diese Stelle

Verbessern Sie Ihre Chancen auf ein Vorstellungsgespräch, indem Sie Ihre Lebenslauf-Bewertung vor der Bewerbung überprüfen.

Lebenslauf hochladen

Foundation EGI

WebsiteLinkedInAlle Stellen

11 - 50 Mitarbeiter

Gegründet 2023

🤖 Künstliche Intelligenz

☁️ SaaS

🏢 Unternehmen

Artificial Intelligence • SaaS • Enterprise

Foundation EGI ist eine KI-gesteuerte Plattform, die die Erstellung von Ingenieurdokumentationen aus 3D-CAD-Daten automatisiert. Das Unternehmen wandelt Baugruppen in 2D/3D-Teilekataloge, GD&T technische Zeichnungen, optimierte Montageabläufe und mehrsprachige Bedienungs-/Serviceanleitungen um, mit dem Ziel, den manuellen Entwurf zu reduzieren und die Produktentwicklung für Produktions- und Ingenieurteams zu beschleunigen. Foundation EGI positioniert sich als Unternehmens-SaaS-Lösung, die maschinelles Lernen und rechnergestütztes Design in industrielle Arbeitsabläufe integriert, um Genauigkeit, Nachverfolgbarkeit und Time-to-Production zu verbessern.

Beschreibung

• 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. • 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. • 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. • 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. • 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.

🎯 Anforderungen

• 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.