Foundation AI Post-Training & Alignment Intern

GE Vernova; Bellevue (WA); On-site; USD 34,000 - 48,000; Full time

Benefits offered by this job

Relocation assistance

Housing allowance

Employee assistance program

Retirement plan

Job summary

GE Vernova's Applied Grid Innovation (AGI) team is pursuing AI-driven solutions for the grid of the future. This Power System Intern role focuses on foundational models, combining power systems with AI to improve operational decisions for utilities.

You will contribute to research, prototyping, and experiments, collaborating with AI researchers and engineers, with the aim of accelerating an AI-native grid responsibly and safely.

Qualifications

• Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Data Science, AI/ML, or related discipline.
• Experience with Python and machine learning frameworks.
• Understanding of supervised learning and deep learning fundamentals.
• Experience conducting research or model development projects.
• Strong analytical and problem-solving skills.

Responsibilities

• Research post-training techniques for foundation models.
• Explore fine-tuning, alignment, reinforcement learning, and feedback-driven learning approaches.
• Develop experiments to improve model performance on utility-specific tasks.
• Evaluate model behavior, accuracy, and domain adaptation.
• Support creation of datasets and training pipelines.
• Analyze tradeoffs between performance, cost, and operational value.
• Collaborate with AI researchers and software engineers.
• Present experimental findings and recommendations.

Skills

Python

ML fundamentals

Research experience

Analytical thinking

Problem solving

Education

Bachelors/Masters/PhD in CS/DS/AI

Tools

PyTorch

Hugging Face

Job description

GE Vernova's Applied Grid Innovation (AGI) team is pursuing AI-driven solutions for the grid of the future. This Power System Intern role focuses on foundational models, combining power systems with AI to improve operational decisions for utilities.

You will contribute to research, prototyping, and experiments, collaborating with AI researchers and engineers, with the aim of accelerating an AI-native grid responsibly and safely.