Primary Responsibilities:

• Assist in designing, developing, testing, and deploying machine learning models.
• Work with large datasets: cleaning, preprocessing, feature engineering.
• Collaborate with data scientists, engineers, and product managers to integrate ML models into applications.
• Help monitor model performance and retrain/update models as needed.
• Contribute to documentation and best practices.
• Stay up to date with the latest ML research, tools, and technologies.
• May require occasional travel (10%), domestic or international.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field and 2 years of work experience or Master's degree with less than 2 years of work experience. May consider additional work experience in lieu of a degree.
• Must have the ability to obtain a Public Trust clearance (US citizenship required).
• Solid understanding of machine learning fundamentals (e.g., supervised/unsupervised learning, model evaluation).
• Proficiency in Python and common ML libraries (e.g., scikit-learn, pandas, NumPy).
• Proficiency in Object-oriented software design.
• Familiarity with PyTorch, or similar frameworks.
• Familiarity with cloud platforms (e.g., AWS, GCP, or Azure).
• Experience with version control tools (e.g., Git).
• Exposure to MLOps concepts or tools (e.g., MLflow, Docker, CI/CD).
• Basic knowledge of SQL and data querying.
• Strong problem-solving and communication skills.
• Eagerness to learn and adapt in a fast-paced environment.

Preferred Qualifications:

• Master’s degree and 1–2 years of hands-on experience in a machine learning or data science role (including internships, research, or full-time industry experience).
• Proven experience building, validating, and deploying machine learning models in real-world scenarios.
• Completed academic or industry projects that demonstrate the application of ML techniques to solve complex problems.
• Cloud platform certifications, such as: Microsoft Certified: Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, Google Cloud Professional Machine Learning Engineer
• Experience using MLOps tools and workflows, including MLflow, Docker, CI/CD pipelines, and model monitoring.
• Familiarity with deep learning frameworks, especially PyTorch, and the ability to build and fine-tune neural network models.
• Exposure to data engineering workflows, such as data pipelines (e.g., Airflow), distributed processing (e.g., Spark), or data lake architectures.
• Strong documentation skills and the ability to clearly communicate technical details to both technical and non-technical audiences.
• Contributions to open-source ML projects, participation in Kaggle competitions, or relevant publications (a plus).

The Leidos Security Enterprise Solutions (SES) team has developed a suite of integrated solutions for aviation, ports, borders, and critical infrastructure customers around the world. We have more than 24,000 products deployed across 120 countries, including best-in-class security checkpoint and inspection systems for people, checked baggage and more.

Check out the links below to learn more about Security Enterprise Solutions (SES)
https://careers.leidos.com/pages/security-enterprise-solutions    
https://www.leidos.com/markets/aviation/security-detection