Multimodal Data Analytics Engineer

Pay Range

Base pay: $/yr - $/yr. Pay will be based on skills and experience.

Are you curious how the cyber‑physical data produced by humans and devices reveal hidden stories about their activity? Are you excited by the challenge of turning massive, messy, and highly disparate real‑world data into actionable insights? Are you searching for meaningful work where your tools and ideas have a direct impact on national security? If so, we’re looking for you to join the Cyber‑Physical Systems Development Group (QNN) at Johns Hopkins Applied Physics Laboratory (APL).

QNN empowers U.S. defense and intelligence operators with unmatched access and situational awareness. We develop, analyze, and evaluate systems that employ machine learning and AI/ML to extract insight from disparate data sets such as commercial telemetry, video, and computer network traffic.

• Conceive, design, and refine analytical approaches that transform complex, multimodal data into meaningful insights.
• Build robust, mission‑focused software tools that operationalize these analytical techniques for research, demonstration, and operational applications.
• Mentor and develop junior staff, providing guidance in both technical execution and professional growth.
• Identify emerging mission needs and propose innovative capabilities and concepts to sponsors.
• Clearly document methods and results, and effectively present findings to stakeholders within the lab and across the broader technical community.

• Possess a Bachelor’s Degree in Computer Science, Data Science/Engineering, Computer Engineering, Artificial Intelligence/Machine Learning, or another field relevant to the duties.
• Have at least 7 years of relevant experience in analytical tool development using AI/ML methods.
• Have demonstrated ability to develop high‑quality software solutions that are accurate, efficient, reliable, maintainable, and scalable.
• Possess strong knowledge of data engineering fundamentals: ingestion, cleaning, transformation, storage, fusion, and visualization.
• Have experience with professional software development practices: version control, testing, code review, and modular design.
• Have strong technical writing, presentation, and communication skills.
• Ability to obtain an Interim Top Secret level security clearance by your start date and ultimately obtain TS/SCI clearance. Eligibility requires U.S. citizenship.

• Master’s or PhD in a relevant technical field.
• Experience processing data types such as video, imagery, audio, mobility data, network data, financial transactions, text records/documents, etc.
• Experience with C, C++, and/or Python and open‑source analysis packages such as Pandas, Geo Pandas, OpenCV, Scapy, Zeek, Tensor Flow, Lang Chain, etc.
• Experience with SQL, No
  
  SQL, object storage, knowledge graphs, Parquet, etc.
• Experience with containerization tools (Docker, Kubernetes).
• Experience with cloud platforms (AWS, GCP, Azure) and cloud/distributed architectures.

The Johns Hopkins University Applied Physics Laboratory (APL) brings world‑class expertise to our nation’s most critical defense, security, space, and science challenges. We celebrate diversity and encourage creativity.

We offer a vibrant, welcoming atmosphere, generous benefits, a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore‑Washington metro area.

All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities.

If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accommodations.