Machine Learning Engineer

Leidos-posted 2 days ago

Full-time • Entry Level

Tewksbury, MA

11-50 employees

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Leidos’ Security Enterprise Solutions (SES) operation is seeking a Machine Learning Engineer to support our data science and AI initiatives. You will help build and maintain ML models and pipelines, collaborate with cross-functional teams, and contribute to deploying scalable machine learning solutions in production. You will be developing new capabilities and intellectual property including (but not limited to) the detection of prohibited concealed items on passengers or in their baggage.We're building intelligent systems that drive real-world impact pushing the limits of what is possible with regards to automated security solutions, and we’re looking for passionate, curious, and collaborative individuals to join our team.

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.

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.

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).

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