ML Ops Engineer

Riverside Overview

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country’s most challenging technical problems.    All Riverside Research opportunities require U.S. Citizenship.  

Position Overview

Riverside Research is looking for a Machine Learning Ops (ML Ops) Engineer to join our AI/ML, Automation, and Augmentation Applications team. The ML Ops Engineer will support the team by designing infrastructure, developing process, deploying, scaling, and maintaining machine learning models while also supporting prototype development. The ML Ops Engineer will play a critical role in supporting bringing prototype capabilities into operational environments. 

Responsibilities

• ML Ops Infrastructure:
  • Design, implement, and manage scalable ML Ops pipelines to automate the training and deployment of machine learning models.
  • Build and maintain pipelines for ML model integration and delivery.
  • Work with the Riverside core IT team to support acquisition, installation, and maintenance of servers and storage.
• Cloud and Containerization:
  • Work with team members and government customers to transition models from R&D to operations environments.
  • Deploy and manage ML models on cloud platforms (primary AWS, secondary Azure, GCP) using containerization tools like Docker and Kubernetes.
  • Ensure high availability, scalability, and cost-efficiency of ML services.

Qualifications

Education/Qualifications

• Must live in or relocate to a commutable distance to Wright Patterson Airforce Base and surrounding areas.
• Bachelor’s degree in computer science, engineering, mathematics, or related field
• 3+ years of experience managing Linux and Unix systems
• 1+ years of experience in ML Ops, DevOps, or a related engineering role.
• Must be a U.S. Citizen and have an active TS/SCI clearance 
• Proven experience with machine learning pipelines and automation tools
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and container orchestration (e.g., Kubernetes, Docker).
• Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch
• Strong understanding of ML Ops best practices, including model versioning, monitoring, and continuous integration.
• Ability to optimize model training and inference performance.
• Familiarity with data pipeline orchestration tools

Preferred Qualifications:

• Master’s degree in computer science, engineering, mathematics, or related field
• 3+ years of experience in ML Ops, DevOps, or a related engineering role

Global Comp

$95,000 - $142,000 This represents the typical compensation range for this position based on experience, location and other factors.

Closing Statement

  Riverside Research Institute is a not-for-profit, technology-oriented defense company, where service to our customers and support of our staff is our overall mission. Riverside is an affirmative action-equal opportunity employer and complies with all applicable federal, state, and local laws regarding recruitment and hiring.  Riverside offers comprehensive compensation and benefit packages to our employees. Riverside bases its employment decisions solely on technical experience, qualifications and other job-related criteria related to our organizational purpose as a not-for-profit company, and without regard to race, color, religion, age, sex marital status, sexual orientation, national origin, physical or mental disability, veteran’s status or any other status legally protected by applicable federal, state, and local law.