Senior Machine Learning Engineer

Leidos offers a competitive compensation package along with a comprehensive benefits suite for an on-site role in Vista, California. This Senior Machine Learning Engineer position focuses on MLOps-enabled object detection for border security, ensuring the development, deployment, and integration of models across operational systems. The role carries a salary range of USD 107,900 to 195,050 per year and provides opportunities to own impactful projects from inception through production.

Benefits

• Competitive compensation
• Health and wellness programs
• Income protection coverage
• Paid leave and time-off benefits
• Retirement savings options

Responsibilities

• Develop, train, and evaluate machine learning models using modern MLOps practices and frameworks
• Design and maintain reproducible training pipelines that support scalable and repeatable experimentation
• Collaborate with cross-functional teams to integrate models into operational systems and workflows
• Optimize model performance and reliability through continuous monitoring, testing, and iteration

Requirements

• MS or PhD in Data Science, Engineering, Applied Science or a similar discipline with at least 10 years of industry experience
• Ability to support the full ML lifecycle, from data preparation and training to deployment and monitoring
• Experience tracking experiments, model performance, and model versioning using a platform like MLflow to ensure transparency and auditability
• Experience with data versioning frameworks such as DVC, MLFlow Dataset, or LakeFs
• Experience deploying and managing machine learning models in production environments using Docker or Kubernetes
• Familiarity with modern data stacks including cloud platforms, data warehouses, and MLOps concepts
• Ability to evaluate technical approaches and guide technical decision-making
• Strong track record of delivery ownership and cross-functional collaboration
• Ability to multitask across projects
• Excellent communication skills, both written and verbal
• Some travel is required (< 25%)
• Ability to obtain and maintain Public Trust access

Technologies

• MLflow, DVC, MLFlow Dataset, LakeFs
• Docker, Kubernetes
• Kubeflow, Airflow
• ResNet, Yolo, U-Net