Additive Manufacturing Data Project Engineer

ASTM International•Washington, DC

About The Position

ASTM is advancing solutions for the rapidly evolving landscape of advanced manufacturing, with a focus on Industry 4.0 and digital transformation. We collaborate with industry, government, and academia to accelerate standards development, workforce training, and certification programs that address emerging challenges and expanding responsibilities in these fields. Building on successful initiatives in areas such as additive manufacturing and Exo Technologies—where ASTM established centers of excellence to speed technology adoption—we are now developing a Data Commons for materials and manufacturing data. This platform will build on the best practices and guidelines for the generation and management of high-pedigree data that have been developed under our Consortium for Materials Data and Standardization (CMDS) program. The Data Commons will curate, structure, and organize high-quality, standardized, AI-ready datasets to drive innovation, enhance product development, and support next-generation manufacturing intelligence. We are seeking an experienced Additive Manufacturing Data Project Engineer to join our team. This role will be a crucial link between unstructured, real-world manufacturing data generated and contributed from a variety of sources and our AI-ready platform. This is a Mid-level role where you will not only write code but also own the definition of additive manufacturing data models and standards. Initially, this is a high-touch consultative role, guiding external contributors. As our digital pipelines mature, your focus will shift toward automation, quality assurance, and architecture.

Requirements

Bachelor’s degree in mechanical engineering, manufacturing engineering, materials science, materials informatics, or relevant field.
3+ years of domain experience developing and qualifying Additive Manufacturing (AM) processes.
Familiarity with AM post-processing methods, inspection methods and materials testing and characterization.
Proficiency in Python for data manipulation, automating workflows, and schema development.
Experience working with scientific data formats (HDF5, NetCDF, CSV/JSON metadata-rich structures, etc.).
Experience with ML pipelines and preparing structured/unstructured data for AI.
Excellent communication skills with the ability to explain technical data requirements to manufacturing process engineers, and manufacturing constraints to software engineers.
Project management experience with exceptional organizational skills
Proven ability to work in a team with good interpersonal skills
Self-starter with exceptional attention to detail and ability to work remotely

Nice To Haves

Advanced degree in mechanical engineering, manufacturing engineering, materials science, materials informatics, or relevant field.
5+ years domain experience in additive manufacturing application development.
3+ years in data science, data management, or related roles.
Experience with SQL.
Demonstrated experience of leading & managing technical projects.
Experience in standards development and participation in technical committees.

Responsibilities

Serve as the primary technical point of contact for external contributors of Additive Manufacturing data.
Manage individual contributor projects, including timelines, milestones, and deliverables.
Work closely with contributors to clean, transform, and curate AM data for ingestion into the Data Commons.
Apply domain knowledge to ensure accurate interpretation of AM data and validate integrity (machine logs, build parameters, material properties, in-process monitoring data, and post-build inspection/test results).
Build models to represent relationships across additive manufacturing materials, processes, and performance outcomes.
Apply Data Commons internal schemas and guidelines to ensure consistency and interoperability across datasets.
Align contributor data with existing industry standards (ASTM F42, ISO/ASTM 52900) while supporting creation of new standards to drive broader industry adoption compatible with Data Commons schemas.
Develop metadata mapping templates and contributor-specific ingestion guides and generate data quality and validation reports.
Provide actionable feedback and recommendations to contributors for improving data collection and formatting.
Maintain internal onboarding documentation and playbooks to streamline future contributor integrations.