Job Description We are seeking a Software Engineer II, Machine Learning Systems, to help deploy and operationalize machine learning solutions used in non-destructive testing applications within the nuclear industry. In this role, you will support the development of machine learning workflows by ensuring they can be packaged, deployed, and maintained in reproducible, stable environments for pilot and production use. This is not a research-focused data science role. Instead, the position centers on machine learning system integration, deployment, infrastructure awareness, and engineering execution. You will work closely with data scientists and subject-matter experts to validate solutions, implement deployment best practices, and integrate machine learning capabilities into existing systems. The environment includes on-premises, regulated, and occasionally air-gapped systems where considerations such as networking, hardware constraints, dependency management, and operational reliability are critical. Projects may involve visual inspection data, ultrasonic signals, eddy current inspection data, and other scientific or industrial data formats. Early projects will focus on executing well-defined deployment and integration efforts. As the team grows, you will have opportunities to help shape deployment standards, reproducibility practices, and system integration patterns across multiple machine learning initiatives.

Compensation:

$40-$60/hr Exact compensation may vary based on several factors, including skills, experience, and education. Employees in this role will enjoy a comprehensive benefits package starting on day one of employment, including options for medical, dental, and vision insurance. Eligibility to enroll in the 401(k) retirement plan begins after 90 days of employment. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements Bachelor's degree in Computer Science, Physics, Software Engineering, Applied Mathematics, Data Science, or a related technical field. Advanced degrees are welcome but not required. 2+ years of professional software engineering experience. At least 1 year of hands-on experience supporting machine learning or data-driven applications. Experience packaging, deploying, or supporting Python-based applications outside of notebook environments. Experience working across Windows and Linux environments. Experience working in on-premises, air-gapped, regulated, or otherwise constrained environments. Understanding of machine learning fundamentals and the requirements for training, inference, and deployment workflows. Ability to collaborate with data scientists, subject-matter experts, and engineering teams to support production-ready ML workflows. Experience building ETL workflows or data adapters for scientific or industrial datasets. Experience deploying ML applications in air-gapped or constrained environments. Experience integrating ML workflows with legacy applications. Experience implementing logging, observability, error handling, and performance monitoring. Experience with object storage, dataset versioning, artifact management, or model synchronization workflows. Experience supporting distributed systems or deployments that span multiple machines. Experience working with binary data such as images, video, signals, ultrasonic inspection data, or eddy current inspection data. Experience with Microsoft Azure. Experience with C++ or C#. Experience supporting UI-facing applications.