2026 Graduate - Software Engineer; Data Scientist; Ontologist - Threat Analytic Systems

Are you excited about applying your education toward building creative solutions for our nation’s greatest challenges? Are you searching for a collaborative environment that prioritizes impact, innovation, and personal development? If so, the Threat Analytic Systems Group is looking for someone like you to join our APL team! We are a team of software engineers, data scientists, and knowledge representation experts who partner with experts across a wide range of domains including countering weapons of mass destruction (CWMD), special operations, intelligence analysis, biothreat security, online disinformation, and disease surveillance. Our goal is to research and build innovative AI-enabled systems that can identify and prioritize relevant data in a timely and scalable manner and accelerate our sponsors’ ability to observe and act on threat activities. We need talented engineers, scientists, and ontologists with creativity, curiosity, and a strong drive for learning to succeed! As a member of our team you will Design, implement, test, and deploy algorithms, machine learning models, and AI-enabled systems in a dynamic, fast-paced, and team-oriented environment. Interact with sponsors, analysts, and other end users to understand their mission goals, gather requirements, and innovate new ways to improve efficiency. Survey academic literature, propose new research, engineer solutions, and utilize DevOps/MLOps strategies to overcome deployment challenges. Document and present work on current research and development activities. Qualifications A Bachelor’s degree in computer science, mathematics, engineering, or related technical field. Have strong problem solving, analytical, and organizational skills, in addition to excellent written and verbal communication skills. Have experience working successfully within a team environment. Are able to obtain an Interim Secret Clearance by your start date and ultimately obtain Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. Software Engineers Experience in computer science fundamentals and Java, Python, JavaScript, and web development. Data Scientists Experience in Python. Familiarity with machine learning and scientific libraries (PyTorch, Tensorflow, Scikit-Learn, Pandas, Numpy). Interest in working with engineers to integrate models and algorithms into analytic pipelines. Ontologists A foundation in formal logic. You’ll go above and beyond our minimum requirements if you have A Master’s degree in computer science, mathematics, engineering or related technical field. Software Engineers Familiarity with Agile software development practices, full life cycle software development, or feature development. Familiarity with continuous integration and continuous deployment (CI/CD) best practices. Familiarity with containerization (Docker, Podman, Kubernetes). Experience with machine learning libraries and techniques. Experience with mobile development and Kotlin. Data Scientists Experience creating experiments, developing approaches, and testing hypotheses for data-driven challenges. Familiarity with Python best practices. Familiarity with foundational machine learning approaches. Experience with information extraction and natural language processing techniques. Ontologists Experience building RDF/OWL ontologies. About Us 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 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 View email address on click.appcast.io. 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 $85,000 Annually Maximum Rate $165,000 Annually #J-18808-Ljbffr Johns Hopkins Applied Physics Lab