Machine Learning Systems Engineer

You will work as an ML Systems Engineer building software for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current). You will own the deployment and lifecycle of machine learning workflows in their pilot and prototype phase and support their integration into legacy workflows for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current).

As the need for nuclear energy grows, the future couldn't be brighter. Join our vital mission to create lasting solutions for our planet's greatest challenges. In this role, you are ready to shape the future of clean energy and make a long-lasting global impact. With us, anticipate engaging and challenging projects that deliver innovative solutions and value-added technologies helping power more than 38 million North American homes. What You'll Do Day-To-Day Work as an ML systems engineer, owning the deployment, reproducibility, and stability of pilot ML workflows in on-prem, airgapped, or regulated environments.

• Support integrating the core ML logic into legacy workflows for non-destructive testing in the nuclear industry (e.g., visual, ultrasonic, eddy current).
• Collaborate with subject-matter experts to validate solutions and follow best practices for packaging, observability, and integration into existing systems.
• Act as a problem solver, providing resolution to a critical problem or seizing an opportunity that improves the satisfaction of a internal or external customers.
• Participates in the testing process through test review and analysis, test witnessing and certification of software.
• Supports and/or installs software applications/operating systems, including necessary third-party dependencies.

• Bachelor's Degree in Computer Science, Physics, Data Science, or Machine Learning or Computer Engineering.
• At least 2 years of related experience is required.
• At least 1 year in a hands-on role in a machine learning Engineering or Data Science context.
• Equivalent work experience may be considered in lieu of degree.
• Expertise in Python for machine learning, with familiarity with scripting languages and either C++ or C#.
• Ownership of reproducible packaging, deployment, and machine learning lifecycle in on-premises environments.
• Experience with implementation of structured logging, error reporting, and performance tracing to support debugging and diagnostics in machine learning workflows.

• Salary: $77,000 - $100,000, and may also include annual incentives and performance bonuses. The base salary range is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location.
• Health & Wellness: Multiple medical plan options, dental & vision coverage, life insurance, long- and short-term disability, and optional supplemental plans (critical illness, accident, hospital indemnity, dependent life).
• Retirement: 401(k) with employer match.
• Paid Time Off: Up to 3 weeks of vacation, 8 sick days, and 13 paid holidays annually.
• Extra Perks: Lifestyle spending account, employer paid mental health support, education reimbursement, professional development opportunities, adoption assistance, parental leave, and employee assistance programs.

About Framatome

Framatome has been shaping the future of nuclear energy in the U.S. for more than 60 years. Framatome Inc. is headquartered in Lynchburg, Virginia, with more than 2,400+ employees across North America supporting nearly every nuclear plant in the nation. From our facilities in Virginia, Washington, North Carolina, Pennsylvania, Massachusetts and beyond, we deliver outage services, advanced nuclear fuel technologies, and next-gen reactor innovations that keep the lights on. At Framatome, you will join a global team committed to creating reliable, carbon-free energy. Here, you can: * Solve complex nuclear challenges that directly impact the climate. * Build your career through technical fellowships, leadership roles, and global opportunities. * Do your best work in a culture that values safety, innovation, and well-being.