GE Vernova - Machine Learning Software Intern - Summer 2027

GE Vernova•Bellevue, WA

About The Position

Addressing the climate crisis is an urgent global priority, and at GE Vernova, we take our responsibility seriously. That is the singular mission of GE Vernova: to continue electrifying the world while simultaneously working to help decarbonize it. We operate with a founder’s mindset. We deliver innovation with passion, speed, and courage. We continuously challenge our thinking and are empowered to dream big and take smart risks. As a student of the advanced analytics team, Software and Controls Solutions, you will be an active contributor to improving energy companies’ plant efficiency and operation through predictive and prescriptive analytics.

Requirements

Enrollment in a Bachelors, Masters, or PhD program in Computer Science, Data Science, Computer Engineering, or related field.
Foundational knowledge in temporal pattern recognition, time-series analysis, or related ML techniques
Experience with Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Ability to interpret technical documentation and contribute to collaborative software development projects.

Nice To Haves

Prior experience with industrial sensor data, anomaly detection, or event classification.
Familiarity with statistical analysis and clustering algorithms.
Familiarity with Jupyter and cloud-based environments.
Knowledge of version control systems (Git) and collaborative project tools (Confluence, Jira).

Responsibilities

Contribute to the design, implementation, and validation of algorithms that improve software alert relevance and operational efficiency.
Collaborate with domain experts to validate detected patterns and ensure alignment with operational realities.
Integrate algorithmic solutions with existing equipment monitoring platforms, ensuring scalability and robustness.
Participate in the evaluation of algorithm performance using documented metrics such as false alarm reduction rates and pattern detection accuracy.
Document all work clearly in accordance with project standards for reproducibility and knowledge sharing.