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
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:November 24, 2025For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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What We Do
We Are Leidos
For 50 years we have been tackling some of the biggest problems that face our nation and our world.
OUR MISSION
Through our culture of innovation and history of performance, we develop deep customer trust built on integrity and create enduring solutions that improve our world. Leidos is a science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. The company’s 43,000 employees support vital missions for government and commercial customers. Headquartered in Reston, Va., Leidos reported annual revenues of approximately $11.09 billion for the fiscal year ended January 3, 2020.
Leidos was cited for the meaningful work employees perform that is challenging, impactful, and aligned with our customers’ missions as reasons professionals want to work and stay at our company. Leidos has also been named to lists including Forbes’ Best Employers for Diversity, Forbes’ America’s Best Employers for Women, Military Times Best for Vets Employers, and Ethisphere Institute’s World's Most Ethical Companies®.
Employees enjoy career enrichment opportunities available through mobility and development and experience rewarding relationships with supportive supervisors and talented colleagues and customers. Employees appreciate our flexible work environment, allowing for and encouraging a true work-life balance. Our professionals are also excited about our Employee Resource Groups, like the newly launched Collaborative Outreach with Remote and Embedded Employees (CORE), which strives to create an environment where every employee, regardless of location, feels fully engaged as a valued employee of Leidos.
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