As a Machine Learning Engineer at Pmat, your day-to-day work will bridge the gap between advanced research and operational military capability. You will be actively involved in the entire lifecycle of AI systems, from initial mathematical formulation to physical deployment on operational platforms.

Your primary technical responsibility will be the hands-on design, development, and implementation of machine learning models and algorithms tailored for naval applications. This includes performing data preprocessing, feature engineering, and rigorous model validation. You will build and maintain cloud-native ML pipelines using AWS, Azure, Docker, and Kubernetes, ensuring that all software is integrated into automated CI/CD and DevSecOps workflows.

Collaboration is a core component of the role. You will work closely with software engineers, data scientists, and mission stakeholders to align machine learning solutions with real-world operational requirements. You will also be responsible for generating technical reports, documentation, and engineering artifacts that comply with both company and government standards.

To be competitive for the Machine Learning Engineer III position, candidates must meet a stringent set of technical, professional, and security requirements.

Must-Have Qualifications

• Security Clearance – An active DoD Top Secret (TS) clearance is mandatory. Candidates must be US citizens with no dual citizenship.
• Education – A Bachelor of Science in Computer Science, Data Science, Geography, Mathematics, Machine Learning, or Statistics (or equivalent years of relevant experience).
• Programming Skills – Strong, hands-on programming skills in Python, along with proficiency in at least one compiled language such as Java, C++, Go, or Rust.
• Machine Learning Experience – Proven experience developing and deploying algorithms, mathematical models, or ML models in real-world applications using TensorFlow, PyTorch, or scikit-learn.
• Cloud & Containerization – Familiarity with cloud platforms (AWS or Azure) and containerization technologies (Docker or Kubernetes).
• Physical Demands – Ability to perform engineering and testing activities aboard Navy vessels, including navigating tight spaces and carrying equipment up to 50 lbs.

Nice-to-Have Qualifications

• Security Upgrade – An active DoD TS/SCI clearance is highly preferred.
• Military Domain Knowledge – Prior experience supporting NAVWAR, NIWC Pacific, or other Navy Command and Control (C2) / ISR programs.
• Advanced MLOps – Experience with distributed computing, parallel processing, and automated CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).
• Specialized Certifications – Additional certifications in cloud architecture, data engineering, GIS, or cybersecurity.

Q: How much software engineering versus pure research is involved in this role? A: This is primarily a software engineering and deployment role. While you will design and implement machine learning models, a significant portion of your time will be spent containerizing models, building cloud-native pipelines, writing clean code in Python or C++, and ensuring systems can run reliably in secure, operational environments.

Q: What is the travel requirement for this position? A: The role requires approximately 15% travel, both CONUS (within the US) and OCONUS (international). This travel is typically for customer engagements, team coordination, and conducting testing or deployment evolutions aboard Navy vessels. A current passport is required.

Q: Where are the primary work locations? A: Positions are associated with specific secure facilities, including Buckley Space Force Base in Denver, CO; Fort Carson and Air Force TENCAP in Colorado Springs, CO; and the NAVWAR Washington Liaison Office and the Pentagon in Arlington, VA. Work is primarily on-site at these secure locations.

Q: How can I stand out during the interview process? A: The best way to stand out is to demonstrate a strong "deliver and demonstrate" mindset. Show that you can write production-quality, secure code, and explain how you would design machine learning systems to operate under the strict hardware, bandwidth, and security constraints typical of DoD environments.

To maximize your chances of success during the Pmat interview process, keep these practical tips in mind:

• Emphasize Resource Constraints – When discussing system design, always consider resource limitations. Explain how you would optimize your models for low-memory, low-bandwidth, or offline edge environments. This shows you understand the operational realities of naval applications.
• Highlight Compiled Language Familiarity – While Python is the primary language for ML development, the job description highly values compiled languages. If you have experience in C++, Java, Go, or Rust, make sure to highlight how you have used these languages to optimize performance-critical code.
• Demonstrate Git and CI/CD Discipline – Pmat values software engineering best practices. Be prepared to talk about your experience with Git workflows, code reviews, automated testing, and secure CI/CD pipelines.

• Prepare for Physical and Environmental Questions – Do not overlook the physical requirements of the job. Be ready to confidently confirm your ability to work aboard Navy vessels, navigate confined spaces, and handle the physical demands of shipboard testing.

Securing a Machine Learning Engineer position at Pmat is an exceptional opportunity to apply cutting-edge AI methodologies to critical national defense and naval missions. The role demands a unique combination of deep machine learning expertise, rugged software engineering discipline, and a commitment to supporting the warfighter in challenging operational environments.

To prepare effectively, focus your efforts on mastering hands-on Python coding, containerization with Docker and Kubernetes, and the mathematical foundations of model optimization for edge deployment. Align your preparation with the "deliver and demonstrate" philosophy that defines Pmat's culture.

The salary ranges reflect the high level of technical expertise and security responsibility required for this position. When preparing your compensation expectations, consider how your specific combination of active security clearance, compiled language skills, and operational AI experience positions you within this competitive framework.

With focused preparation, a strong command of software engineering best practices, and a clear understanding of the mission-critical nature of the work, you can position yourself as an outstanding candidate. For additional insights, practice questions, and peer interview reviews, utilize the resources available on Dataford to finalize your preparation strategy. Good luck!