Machine Learning Engineer

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.

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.

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.