Saic
Job Title:
Secret Cleared AI/Machine Learning Subject Matter Expert
Location:
San Diego, CA, United States
Schedule:
Full-time, Day Job
Travel:
No
Minimum Clearance Required:
Secret
Preferred Clearance:
TS/SCI
Remote Work:
On-Site
SAIC is seeking a Secret cleared AI/Machine Learning Subject Matter Expert in support of NAVWAR’s Naval Operational Architecture (NOA) Program in San Diego, CA. In this mission‑critical role you will help drive the innovation of autonomous, AI‑enabled systems that support distributed maritime operations. You will translate sensor‑derived data into real‑time decision triggers, enhancing the Navy’s command networks with AI‑driven analysis and prioritizing speed, resilience, and operational superiority.
Responsibilities:
Design and implement computer vision algorithms for object detection, tracking, and environmental perception.
Analyze and integrate data from various sensor modalities, including EO/IR cameras, LIDAR, and radar.
Develop scalable data pipelines and orchestrate workflows using Apache Airflow and AWS services.
Write efficient Python code and shell scripts for deployment and automation tasks.
Work in a Linux development environment, emphasizing scripting, debugging, and system‑level operations.
Collaborate with cross‑functional teams including ML engineers, software developers, and systems integrators.
Qualifications
Education and Experience:
Master’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related STEM field and six (6) years of AI/ML experience in defense or intelligence environments.
OR Bachelor's degree in the above fields plus at least eight (8) years of relevant defense‑oriented AI/ML experience.
Citizenship and Clearance Requirements:
US Citizenship required.
Active Secret clearance required; TS/SCI clearance preferred.
Required Skills and Experience:
Six (6) or more years of experience in Machine Learning and Computer Vision.
Proficiency with Python and Bash scripting.
Experience with Apache Airflow and AWS Cloud for data orchestration and workflow automation.
Practical experience working with Unmanned Surface Vehicles (USVs) or similar autonomous platforms such as UAVs, UUVs, and ISR platforms.
Hands‑on experience with computer vision libraries such as OpenCV, PyTorch, TensorFlow, etc.
Comfortable operating in Linux environments for development and deployment tasks.
Preferred Skills and Experience:
Proficiency in containerization and orchestration tools (e.g., Docker, Kubernetes) for scalable deployment.
Understanding of ML Ops practices including CI/CD pipelines, model versioning, and automated retraining.
Experience with real‑time systems and latency‑sensitive applications in robotics or surveillance.
Prior work with defense, maritime, or aerospace systems, especially involving autonomy or ISR missions.
Target Salary Range:
$160,001 – $200,000 (typical range based on experience and other factors).
SAIC is an Equal Opportunity Employer.
Are you an SAIC Employee? Please apply through the internal career site here >
#J-18808-Ljbffr
Secret Cleared AI/Machine Learning Subject Matter Expert
Location:
San Diego, CA, United States
Schedule:
Full-time, Day Job
Travel:
No
Minimum Clearance Required:
Secret
Preferred Clearance:
TS/SCI
Remote Work:
On-Site
SAIC is seeking a Secret cleared AI/Machine Learning Subject Matter Expert in support of NAVWAR’s Naval Operational Architecture (NOA) Program in San Diego, CA. In this mission‑critical role you will help drive the innovation of autonomous, AI‑enabled systems that support distributed maritime operations. You will translate sensor‑derived data into real‑time decision triggers, enhancing the Navy’s command networks with AI‑driven analysis and prioritizing speed, resilience, and operational superiority.
Responsibilities:
Design and implement computer vision algorithms for object detection, tracking, and environmental perception.
Analyze and integrate data from various sensor modalities, including EO/IR cameras, LIDAR, and radar.
Develop scalable data pipelines and orchestrate workflows using Apache Airflow and AWS services.
Write efficient Python code and shell scripts for deployment and automation tasks.
Work in a Linux development environment, emphasizing scripting, debugging, and system‑level operations.
Collaborate with cross‑functional teams including ML engineers, software developers, and systems integrators.
Qualifications
Education and Experience:
Master’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related STEM field and six (6) years of AI/ML experience in defense or intelligence environments.
OR Bachelor's degree in the above fields plus at least eight (8) years of relevant defense‑oriented AI/ML experience.
Citizenship and Clearance Requirements:
US Citizenship required.
Active Secret clearance required; TS/SCI clearance preferred.
Required Skills and Experience:
Six (6) or more years of experience in Machine Learning and Computer Vision.
Proficiency with Python and Bash scripting.
Experience with Apache Airflow and AWS Cloud for data orchestration and workflow automation.
Practical experience working with Unmanned Surface Vehicles (USVs) or similar autonomous platforms such as UAVs, UUVs, and ISR platforms.
Hands‑on experience with computer vision libraries such as OpenCV, PyTorch, TensorFlow, etc.
Comfortable operating in Linux environments for development and deployment tasks.
Preferred Skills and Experience:
Proficiency in containerization and orchestration tools (e.g., Docker, Kubernetes) for scalable deployment.
Understanding of ML Ops practices including CI/CD pipelines, model versioning, and automated retraining.
Experience with real‑time systems and latency‑sensitive applications in robotics or surveillance.
Prior work with defense, maritime, or aerospace systems, especially involving autonomy or ISR missions.
Target Salary Range:
$160,001 – $200,000 (typical range based on experience and other factors).
SAIC is an Equal Opportunity Employer.
Are you an SAIC Employee? Please apply through the internal career site here >
#J-18808-Ljbffr