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Data Encoder Jobs in Boston, MA (NOW HIRING)

Jacobs

VDC Coordinator

Boston, MA · On-site

Post deliverables utilizing the Autodesk Construction Cloud (ACC) platform. * Assist in model quality checking, digital close-out and asset data encoding for handover. * Work with point clouds in ...

Boston Medical Center

Inpatient Lead Coder

Boston, MA · On-site

... encoder and EPIC. * Assists IP Coding Manager with special projects as needed. * Reviews patient medical records and abstracts medical data that identifies all diagnoses and procedures. * Codes ...

Jacobs

VDC 4D Coordinator

Boston, MA · On-site

Develop and maintain 4D models for construction projects, integrating schedule data with 3D models ... Strong Understanding of Revit Parameter Management and encoding to align model elements with ...

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$10

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How much do data encoder jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for data encoder in Boston, MA is $34.97, according to ZipRecruiter salary data. Most workers in this role earn between $13.12 and $65.88 per hour, depending on experience, location, and employer.

What is a Data Encoder job?

A Data Encoder is responsible for inputting, updating, and maintaining accurate data in computer systems or databases. They ensure data integrity by verifying and correcting information as needed. The role often involves handling confidential records, organizing files, and generating reports. Strong attention to detail, typing skills, and familiarity with data management software are essential for this position.

What are the key skills and qualifications needed to thrive in the Data Encoder position, and why are they important?

To thrive as a Data Encoder, you need excellent attention to detail, fast and accurate typing skills, and a high school diploma or equivalent as a common minimum qualification. Familiarity with data entry software, spreadsheet applications like Microsoft Excel, and sometimes database management systems is typically required. Strong organization, time management, and the ability to work independently or as part of a team are valuable soft skills in this role. These skills are crucial for ensuring the accuracy, reliability, and efficiency of data processing tasks within various industries.

What are the typical daily responsibilities of a Data Encoder?

A Data Encoder is primarily responsible for accurately inputting and updating information into digital databases or systems, often working with large volumes of data from paper or electronic sources. Typical daily tasks include reviewing documents for errors, verifying data for completeness and accuracy, and organizing files for easy retrieval. Data Encoders may also collaborate closely with other administrative staff or departments to ensure that records remain up-to-date and accessible. In some organizations, they also assist with basic data analysis or generate routine reports to support business operations.

What are the most commonly searched types of Data Encoder jobs in Boston, MA? The most popular types of Data Encoder jobs in Boston, MA are:
What are popular job titles related to Data Encoder jobs in Boston, MA? For Data Encoder jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Data Encoder jobs in Boston, MA look for? The top searched job categories for Data Encoder jobs in Boston, MA are:
Data Encoder jobs near you

Mechanical Data Engineer (Mechanical + Data Engineering Required)

Foundation EGI

Boston, MA • Remote

Full-time

Posted 10 days ago

Job description

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.

We 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.