Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. The Machine Learning Engineer will support data science and AI initiatives by building and maintaining ML models and pipelines, collaborating with cross-functional teams, and deploying scalable machine learning solutions in production.
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.
Qualification
Required
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
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).
Benefit
Health and Wellness programs
Income Protection
Paid Leave
Retirement
Leidos is a Fortune 500® innovation company rapidly addressing the world’s most vexing challenges in national security and health.
