Software Engineering Internship; MLOps & Deep Learning Infrastructure

Job Description

About Company:

Deca Defense exclusively serves Defense OEMs and the DoD, specializing in Edge AI and autonomous systems engineering. Their core mission is to eliminate warfighter pain points by developing custom autonomous solutions that directly resolve critical field challenges. They achieve this by integrating advanced autonomy with embedded AI to deliver precise, mission-critical solutions at the tactical edge. A distinguishing feature of Deca Defense is its team, which comprises veterans who have transitioned from the battlefield. These individuals contribute invaluable real-world insights from various deployments directly to the solutions, ensuring that the autonomy-driven solutions are tailored to the harsh realities of the tactical edge.

Job Description:

• Job Title: Software Engineering Internship; MLOps & Deep Learning Infrastructure
• Company: Deca Defense
• Location: Remote
• Employment Type: Unpaid Internship (25 hours per week)
• ITAR Restriction: Due to International Traffic in Arms Regulations (ITAR), only U.S. citizens are eligible to apply.

Position Overview:
This is an unpaid internship for a Software Engineering Intern (MLOps & Deep Learning Infrastructure). The intern will support the development of internal AI tooling, model training pipelines, and distributed deployment systems. The role is designed for technically driven individuals interested in the journey of machine learning models from research to operational deployment, particularly within environments constrained by hardware, bandwidth, or security. The intern will work alongside engineers developing MLOps infrastructure for autonomous systems at the tactical edge.

Key Learning Opportunities and Responsibilities:

• Foundations of Internal Tooling: Learn how Python-based tools are utilized for model training, experiment tracking, and dataset management.
• MLOps Lifecycle Exposure: Observe and participate in the supervised development of components across the data ingestion, preprocessing, training, and deployment pipelines.
• Deep Learning Frameworks: Gain practical familiarity with PyTorch and TensorFlow, including training fundamentals and model evaluation.
• Distributed Model Execution Concepts: Learn about deploying AI models across multiple edge devices, focusing on efficiency and synchronization.
• Automation Fundamentals: Practice writing basic Python scripts and APIs to automate specific parts of ML workflows.
• System Monitoring: Learn to visualize and track model and system performance using logging tools and lightweight dashboards.
• Integration & APIs: Gain exposure to integrating systems with MLOps tools.
• Software Development Practices: Learn version control (Git), modular programming, and CI/CD fundamentals within a supervised engineering environment.

Preferred Qualifications and Learning Objectives:

• Educational Background: Pursuing or recently completed a degree in Computer Science, Software Engineering, or a related technical field.
• Programming Skills: Strong Python proficiency, including experience with API design, data structures, and scripting.
• Deep Learning Exposure: Basic knowledge of PyTorch or TensorFlow, coupled with an interest in model design, training, and optimization.
• MLOps Foundations: Familiarity with or curiosity about technologies such as Docker, Kubernetes, MLflow, or Airflow.
• Distributed Systems: Interest in understanding how models are deployed, managed, and scaled across multiple edge devices.
• Version Control: Comfortable using Git and participating in collaborative workflows.
• Learning Goals: Develop practical skills in MLOps, PyTorch-based model development, distributed deployment, and deep learning infrastructure engineering.

Why Join Deca Defense:

• Work directly on projects that support AI autonomy in real defense applications.
• Learn how MLOps, infrastructure, and autonomy intersect at the tactical edge.
• Build tangible skills and artifacts that demonstrate technical depth in applied AI engineering.
• Collaborate with a team where practical engineering expertise meets real-world mission experience.