LEAD SYSTEMS ENGINEER

Full Job Description

You will serve as an ENGINEER/SCIENTIST - Lead Systems Engineer for Test and Evaluation of Autonomy and Artificial Intelligence Systems in the Weapons Control & Integration Department of NAVSURFWARCENDLD.

In addition to the Basic Requirements for this position, your resume must also demonstrate at least one year of specialized experience at or equivalent to the next lower pay band, ND-04 (GS-12/13 equivalency), in the Federal service or equivalent experience in the private or public sector.

Specialized experience must demonstrate the following: As a professional Engineer or Scientist with experience leading Artificial Intelligence and Machine Learning projects.

Examples of qualifying experience may include: Demonstrated ability to lead the design, development, and implementation of software test tools.

Deep understanding of the entire lifecycle of Al/ML models, including data ingestion, model training, evaluation, validation, deployment, and continuous monitoring.

Demonstrated experience overseeing the entire software development lifecycle by applying Agile methodologies and a DevSecOps framework to enforce best practices for quality and cybersecurity.

Expertise in providing technical and project management leadership, including developing project plans, managing finances and task execution, and providing oversight to contractors and government software development teams.

Demonstrated ability to analyze project requirements, develop methods of test, test execution, analyze data and report results in support warfare system development.

Proven ability to communicate orally effectively and professionally and in writing with peers, sponsor organizations, key stakeholders, high-level management, and industry partners on behalf of your organization and the Navy.

Additional qualification information can be found from the following Office of Personnel Management website: https://www.opm.gov/policy-data-oversight/classification-qualifications/general-schedule-qualification-standards/#url=List-by-Occupational-Series General Engineering Series, 0801 General Physical Science Series, 1301 Operations Research Series, 1515 Mathematics Series, 1520 Computer Science, 1550 Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., professional, philanthropic, religious, spiritual, community, student, social).

Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment. Major Duties:

• You will work to establish rigorous and comprehensive TE (Test and Evaluation) for automated systems, ensuring the highest levels of reliability, safety, and mission success in the most challenging and complex environments.
• You will develop and establish a network of key partnerships for data acquisition and development of data management tools to support AAI (Artificial Intelligence Systems)-enabled systems.
• You will develop proposals to address and close identified capability gaps and manage related projects.
• You will coordinate efforts with the E40 AAI TE Lead, H60 Software Architect, and other relevant technical staff and project leads across NSWC Dahlgren.
• You will partner with other DoD TE organizations to define a cohesive Joint Service plan for AAI verification, validation, and certification.
• You will work with the DoD TE Community of Practice (CoP), the Modeling and Simulation (MS) Community of Practice (CoP) and the Artificial Intelligence (Al) /Machine Learning (ML) CoP to establish common practices and share lessons learned.
• You will work with the USV Autonomy Baseline Lead and the Weapon Autonomy Technical Lead to define realistic use cases for AAI-enabled systems and use those to assess AAI TE requirements.
• You will provide direction and requirements to MS community developing Live, Virtual, and Constructive (LVC) test capabilities for AAI-enabled systems.
• You will provide input and guidance for development of the Machine Learning Operations (ML Ops) pipeline required for data management and enabling AAI-capabilities.