Mechanical Data Engineer (Mechanical + Data Engineering Required) 3 Month Contra
我们是一个来自麻省理工学院的创业公司，由谷创业公司成立。我们是设计和制造的 AI 副驾驶员。我们的使命是从根本上重新发明物理产品如何设计和构建，大大加速产品开发速度。作为数据工作室团队中的个人贡献者，你将在转化原始客户数据成为结构化的高真度数据集中发挥关键作用，这些数据集可以推动模型培训，评估和客户交付。这个角色非常实用，位于产品，研究和工程的十字路口。你将应用你的机械工程和制造专业知识，以创建数据管道，标签工作流程，参考模型和质量检查，确保我们的 AI 系统的准确性和可靠性。机械工程或制造设计经验是必不可少的；没有这种背景的候选人不会被考虑。关键责任 · 1.数据创造，处理和质量 · 用于人工智能训练和推断来吸收，清洁，转换和结构化客户和内部生成的工程数据。· 在 CAD 中设计和构建高品质的机械组件和装配，以作为评估和培训人工智能系统的权威基础真理。· 制作标记数据集，参考设计，注释，爆炸视图，序列和其他工程文物，编码现实理性。·应用工程判断来定义和评估数据集中的输出质量。·不断完善元数据，注释和模型质量标准，保持 ME 数据集的活定义质量。• 2.工作流与工具贡献。· 与产品管理人员合作塑造用于注释，数据纠正，模型输出审查和管道自动化使用的工具。·提供有关工具可用性，工作流效率和自动化的详细反。·帮助开发可扩展，可重复的数据流程，以提高吞吐量和数据一致性。· 3.跨功能合作。·与工程和研究团队密切合作，了解模型数据要求，故障模式以及需要新数据的领域。·通过提供代表性的工程例子和实质性的机械设计影响模型行为。· 与面向客户的团队合作，将域名需求，行业标准和客户数据方案转化为可操作的数据集规格。· Se
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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 Responsibilities
- 1. Data Creation, Processing & Quality
- 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.
- 2. Workflow & Tooling Contributions
- 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.
- 3. Cross-Functional Collaboration
- 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.
- 4. Domain Expertise & Reference Content Creation
- 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.
- 5. Customer & Project Support
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- Experience 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 environments.
- Comfort with customer-facing discovery or solutioning.
What Success Looks Like
- Deliver high-quality datasets that measurably improve model performance.
- Drive standardization and reliability across ME datasets, CAD models, workflows, metadata, and annotations.
- Enable faster model training, evaluation, and deployment through strong cross-functional collaboration.
- Maintain clear documentation, repeatable processes, and continuous quality improvement.
- Be recognized as a trusted ME expert in data quality and domain insight.
Originally posted on Himalayas