Multimodal Data Analytics Engineer

Description

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 someone like you to join our team at APL.

The Cyber-Physical Systems Development Group (QNN) at APL is seeking a Multimodal Data Analytics Engineer to develop and deploy novel data analytic capabilities to our sponsors in support of this mission. QNN is a multi-disciplinary team of scientists and engineers seeking to empower U.S. defense and intelligence operators with unmatched access and situational awareness in any operational environment. We develop, analyze, and evaluate systems that employ a range of analytical techniques, including as machine learning and artificial intelligence (AI / ML), to extract insight from disparate data sets such as commercial telemetry data, video content, and computer network traffic.

As a Multimodal Data Analytics Engineer

• 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.

Qualifications

You meet our minimum qualifications for the job if you...

You'll go above and beyond our minimum requirements if you...

A Master's or a PhD in a relevant technical field.

Experience processing relevant 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 data analysis packages such as Pandas, GeoPandas, OpenCV, Scapy, Zeek, TensorFlow, LangChain, etc.

Experience with a variety of data storage and retrieval methods such as SQL, NoSQL, object storage, knowledge graphs, Parquet, etc.

Experience with containerization tools (e.g., Docker, Kubernetes).

Experience with cloud platforms (e.g., AWS, GCP, Azure) and cloud / distributed software architectures.

About Us

Why Work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including 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. Learn more about our career opportunities at http : / / www.jhuapl.edu / careers .

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@jhuapl.edu .

The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education / training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and / or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.

Minimum Rate

$102,500 Annually

Maximum Rate

$227,500 Annually