Senior Naval ML Engineer – Cloud-Native & DoD

The Marlin Alliance, Inc.; San Diego (CA); Hybrid; USD 140,000 - 195,000; Full time

Job summary

The Marlin Alliance, Inc. in San Diego, CA seeks a Machine Learning Engineer III to design and deploy ML models for naval applications, leading architecture and deployment in distributed Navy environments.

You will develop cloud-native ML pipelines using AWS/Azure, Docker, Kubernetes, and leading ML frameworks. Travel to customer sites and strong security posture are required.

Collaboration with cross-disciplinary teams and adherence to PMAT/government standards is essential for success.

Qualifications

• 10+ years in data science, data engineering, ML engineering or software engineering.
• Proven experience deploying ML models in real-world/naval contexts.
• Strong Python programming and cloud/containerization skills.
• Experience with ML frameworks TensorFlow, PyTorch, scikit-learn.
• Familiarity with CI/CD and Git-based workflows.

Responsibilities

• Design, develop, and implement ML models for naval applications.
• Deploy algorithms and models into real-world operational environments.
• Perform data preprocessing, feature engineering, evaluation, validation.
• Collaborate with engineers and stakeholders to align ML with requirements.
• Develop cloud-native ML pipelines using AWS, Azure, Docker, Kubernetes.
• Implement ML with frameworks such as TensorFlow, PyTorch, scikit-learn.
• Contribute to distributed computing and parallel processing optimization.
• Participate in CI/CD, DevSecOps workflows and documentation.

Skills

Python

Cloud platforms

Docker

Kubernetes

TensorFlow

PyTorch

scikit-learn

Git

CI/CD

Java/C++/Go/Rust

Distributed computing

Education

B.S. in AI/DS/CS/Statistics

Tools

AWS/Azure

Job description

The Marlin Alliance, Inc. in San Diego, CA seeks a Machine Learning Engineer III to design and deploy ML models for naval applications, leading architecture and deployment in distributed Navy environments.

You will develop cloud-native ML pipelines using AWS/Azure, Docker, Kubernetes, and leading ML frameworks. Travel to customer sites and strong security posture are required.

Collaboration with cross-disciplinary teams and adherence to PMAT/government standards is essential for success.