Artificial Intelligence​/Machine Learning Lead Verification and Validation Engineer

Position: Artificial Intelligence / Machine Learning Lead Verification and Validation Engineer
## Artificial Intelligence / Machine Learning Lead Verification and Validation Engineer Apply locations:
Penn State University Parktime type:
Full time posted on:
Posted Todayjob requisition :
REQ #
** APPLICATION INSTRUCTIONS:*** ## CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.* ## CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
* ## If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants.###
** Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.**###   
** POSITION SPECIFICS
** We are searching for a talented, experienced, organized, and a highly motivated AI/ML Engineer to join the Visualization and Decision Support Division within the All-Domain Analytics and Signatures Office (A2SO) at the Applied Research Laboratory (ARL) at Penn State.  You will lead our Independent Verification and Validation (IV&V) and Data Curation Center of Excellence. This team serves as the foundation for effective machine learning—building, curating, and governing the data that directly determines whether AI/ML models succeed in operationally relevant environments.

This position can be filled in
** State College, PA
** or
** Reston, VA**.ARL is an authorized DoD Skill Bridge partner and welcomes all transitioning military members to apply.
** You will:
*** Serve as the technical lead for the IV&V and Data Curation Center of Excellence, setting priorities, defining standards, and driving the team's technical direction
* Mentor, guide, and coordinate a team of engineers performing data labeling, annotation, curation, and IV&V across multiple data modalities
* Establish and evolve annotation standards, labeling guidelines, data governance frameworks, and quality assurance processes to ensure consistency and reproducibility at scale
* Design and oversee data pipelines for ingestion, transformation, quality control, and cataloging of classified and open-source data
* Lead IV&V efforts on datasets and model outputs, defining acceptance criteria and fitness-for-use standards
* Serve as the primary point of contact between the data curation team and research scientists, algorithm developers, and program stakeholders to translate data needs into actionable plans
* Present findings, status, and recommendations to leadership and sponsors through well-constructed briefings, papers, and technical artifacts
* Evaluate and introduce tools, platforms, and best practices for annotation (e.g., CVAT, Label Studio), data versioning, and metadata management
* Drive automation of manual data analysis and curation processes to improve efficiency and scalability
* Architect and maintain data catalogs, metadata schemas, and access-controlled repositories that make curated datasets discoverable and accessible to the broader research team
* Work within an Agile development environment within a large research team
** Required Skills/Experience areas include:
*** Bachelor's Degree in Computer Science, Data Science, Information Science, Engineering, or a related technical field
* Extensive experience designing and managing data curation, annotation, or IV&V workflows for AI/ML systems
* Deep understanding of machine learning fundamentals, including how data quality, labeling strategies, and dataset composition impact model training, evaluation, and deployment
* Strong proficiency in Python and SQL for data manipulation, pipeline development, and analysis
* Experience with data annotation tools and platforms (e.g., CVAT, Label Studio, or equivalent)
* Success in environments where various forms of communication and organizational skills were crucial to be effective
** Preferred Skills/Experience areas include:
*** Prior…