Technical Engineering Project Manager

• Lead end-to-end execution of data labeling projects, including defining labeling standards, workflows, quality criteria, and acceptance thresholds in support of machine learning initiatives

• Perform data labeling and annotation across relevant data types, setting the standard for quality and consistency on the team

• Lead, mentor, and coordinate a team of junior analysts and interns, assigning work, reviewing output, and ensuring labeling accuracy, throughput, and timely delivery

• Develop and maintain labeling guidelines, taxonomies, and documentation to ensure consistency and repeatability across labelers

• Establish and monitor quality control processes, including inter-annotator agreement checks, spot audits, and error correction workflows

• Partner with data scientists, engineers, and research leads to understand model requirements and translate them into effective labeling schemas and datasets

• Track project progress, throughput, and quality metrics; report status and escalate risks or blockers as needed

• Ensure all data handling, labeling, and storage practices comply with applicable cybersecurity, information governance, and data-handling requirements

• Develop a strong understanding of the mission and the problems being solved, and apply that context to improve labeling quality and prioritization

• Support the dissemination of research and results to the broader technical and operational community, leveraging professional networks and outreach where possible

• Identify and recommend improvements to labeling tools, processes, and workflows to increase efficiency and quality

• Coordinate schedules, priorities, and resources across the labeling effort and with dependent project teams

• Demonstrated experience leading or performing data labeling, annotation, or data preparation for machine learning projects

• Past success leading, mentoring, or coordinating a team, including junior staff and/or interns

• Data quality principles and quality control methods (e.g., inter-annotator agreement, audit workflows)

• Familiarity with machine learning concepts and how labeled data supports model training and evaluation

• Strong understanding of the defense mission, including the operational problems that applied research seeks to address

• An existing professional network/connections and experience helping share or transition research to the broader technical or operational community

• Success supporting the military, IC, federal customers, in a security-controlled environment

• Familiarity with cybersecurity, information governance, and data-handling practices (e.g., NIST RMF)

• Success in environments where various forms of communication and organizational skills were crucial to be effective

• Current eligibility for access to classified information at the TS/SCI level or higher and may be subject to a government background investigation to upgrade clearance eligibility, if required

ARL’s purpose is to research and develop innovative solutions to challenging scientific, engineering, and technology problems in support of the Navy, the Intel Community (IC), and other federal government customers.

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