Job Description
Pay Range $40/hr-$60/hr
You will work as an ML systems engineer building software for non-destructive testing in the nuclear industry, including visual, ultrasonic, eddy current, and other inspection-data workflows. Your role will be to support the development of ML workflows by ensuring that they can be deployed in reproducible, stable workflows for pilot use.
This is not a pure data scientist role and is not focused on core model research. The primary focus is ML system integration, deployment, reproducibility, and infrastructure-aware engineering in constrained environments.
Early Growth
Expected after 3-6 months; used to rank, not reject.
Begins standardizing deployment and reproducibility practices across multiple projects to shorten setup time for future work.
Contributes informed input into architectural decisions related to artifact storage, configuration patterns, and pipeline structure in collaboration with data scientists.
Identifies recurring integration challenges with legacy systems and proposes pragmatic improvements within existing ownership boundaries.
Develops stronger intuition for failure modes in pilot deployments and proactively mitigates them before field testing.
Builds practical working knowledge of secure, air-gapped, or on-prem deployment realities, including dependency management, offline install processes, storage limitations, permissions, and data-access constraints.
Helps data scientists move workflows from exploratory development into stable, repeatable pilot deployments.
Long-Term Development
Direction-setting; tie-breaker between strong candidates.
Takes ownership of reproducibility and deployment standards across multiple projects. Moves from stabilizing individual workflows to defining consistent conventions for configuration management, artifact handling, and pilot deployment that reduce setup time and ambiguity for future projects.
Begins shaping integration boundaries in collaboration with Applied ML Engineers, data scientists, and legacy system owners. Contributes to defining stable training and inference interfaces, data contracts, and runtime assumptions that reduce coupling and minimize future rewrite risk.
Proactively identifies and mitigates recurring failure modes in pilot environments.
Develops intuition for hardware variability, data inconsistencies, runtime edge cases, and integration friction, and introduces pragmatic safeguards and diagnostics before issues surface in field testing.
Helps define practical standards for ML workflow deployment in secure nuclear inspection environments where data ownership, network segmentation, limited cloud access, and customer-controlled datasets shape system design.
Supports the long-term maturation of ML systems infrastructure without becoming responsible for enterprise-scale data lake ownership or broad organizational cloud architecture.
You'll collaborate with subject-matter experts and data scientists to validate solutions and follow best practices for packaging, observability, and integration into existing systems. This role will help productize ML workflows so they can operate reliably outside exploratory notebooks and within secure, on-prem, limited-cloud, or air-gapped environments.
Early projects will have well-defined scopes focused on technical execution, with more flexibility and responsibility for shaping deployment patterns as the team grows.
The work may involve binary inspection data such as images, video, signals, ultrasonic data, eddy current data, and other scientific or industrial formats. The role will also require awareness of networking, hardware, data architecture, and deployment limitations in secure environments.
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.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
Bachelor's degree in Computer Science, Physics, Software Engineering, Applied Mathematics, Data Science, or equivalent degrees with the appropriate technical background; advanced degrees, including MS or PhD, welcome but not expected.
2-5 years of professional software engineering experience, with at least 1 of those years in a hands-on role in a machine learning engineering, ML systems, platform engineering, systems engineering, DevOps, or data engineering @context.
Demonstrated experience packaging, deploying, or supporting data-driven or ML-based Python systems beyond exploratory notebooks across Windows and Linux environments.
Comfortable working in on-prem, air-gapped, regulated, or otherwise constrained systems.
Experience with deployment-focused engineering practices such as environment isolation, dependency management, reproducible execution, data versioning, artifact management, ETL/ELT pipelines, and operational support for ML or data-driven workflows
Familiarity with secure or constrained infrastructure where cloud usage may be limited or unavailable
Practical understanding of how networking, hardware, storage, data movement, and runtime constraints affect ML workflow deployment. Preferred Skills
Build data adapters or ETL/ELT workflows to convert legacy scientific/industrial formats into ML-ready datasets; support local artifact and dataset management in constrained environments.
Work with binary data formats such as images, video, signals, ultrasonic inspection data, eddy current data, and other non-destructive testing outputs
Embed core ML logic into reproducible training and inference pipelines, ensuring clean separation of configuration, integration code, and runtime concerns.
Package and deploy ML code for stable execution in constrained or air-gapped environments, managing dependencies, environment isolation, and offline realities, with deployment targets ranging from edge devices to HPC environments.
Integrate ML workflows with legacy applications by capturing runtime constraints, hardware considerations, and deployment limitations.
Implement structured logging, error handling, and performance tracing to support debugging and diagnostics without overengineering.
Configure and use on-prem object stores, data/versioning tools, and model synchronization workflows.
Support local or on-prem data and artifact management tools, potentially including MinIO, LakeFS, or similar technologies
Understand the limitations of working with segregated secure networks, customer-owned data, and environments where data movement or reuse may be restricted.
Support ML workflow deployment without relying on streaming-data architectures such as Kafka or large-scale cloud data platforms.
