GE Aerospace Research - Knowledge Discovery & High Assurance Systems Fellow Intern

GE Aerospace•Town of Niskayuna, NY

6d•Onsite

About The Position

The Knowledge Discovery & High Assurance Systems organization is seeking PhD student interns to advance research and prototyping at the intersection of large language models (LLMs), natural language processing (NLP), vision-language models (VLMs), and knowledge graphs. Interns will help design, implement, and evaluate models and systems that combine unstructured text, visual data, and structured knowledge to deliver trustworthy, high-impact AI capabilities for the aerospace industry. Role Overview Contribute to research on multimodal reasoning, retrieval-augmented generation, and grounding LLMs with knowledge graphs Build end-to-end prototypes: data pipelines, model training/fine-tuning, evaluation, and front-end demos Collaborate with researchers and engineers to translate research into production-ready components

Requirements

Currently enrollment in a PhD program in Computer Science, Electrical Engineering, AI/ML, or related field
Permanent legal authorization to work in the US is required. This role is restricted to U.S. persons. GE will require proof of status prior to employment.
Must be willing to work from the GE Aerospace Research office in Niskayuna, NY.
Minimum GPA 3.0 / 4.0 scale

Nice To Haves

Research experience in at least one of: LLMs, NLP, vision-language/multimodal learning, or knowledge graphs
Strong programming skills in Python and JavaScript
Hands-on experience with modern AI/ML frameworks and tooling: PyTorch, TensorFlow/JAX, Hugging Face ecosystem (Transformers, Datasets, PEFT), OpenAI/Anthropic/Vertex AI SDKs
Vector databases and retrieval tooling, LangChain/LlamaIndex
Data and experiment, Docker, Git
Front-end and back-end development skills: React and Node.js
Solid understanding of ML fundamentals: optimization, evaluation metrics, overfitting/regularization, data leakage prevention
Publications or preprints in top venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV)
Experience with: Multimodal fusion and grounding; VLMs (e.g., LLaVA) Knowledge graph construction and query (RDF/OWL, SPARQL, Neo4j), graph learning (PyG, DGL) Prompt engineering, function/tool use, and agentic workflows RAG evaluation (faithfulness, grounding, hallucination detection) and safety alignment MLOps for research: reproducible pipelines, CI/CD, containerization
Familiarity with security, privacy, and compliance considerations for AI systems
Demonstrated improvement on benchmark tasks
High-quality prototypes and demos
Clear, reproducible research artifacts (papers, code, datasets, and documentation)
Effective collaboration and communication across research and engineering

Responsibilities

Design, implement, and evaluate LLM/NLP/VLM models (pretraining, fine-tuning, instruction tuning as appropriate)
Develop and integrate knowledge graphs for reasoning, validation, and explainability (entity/relation extraction, ontology alignment, graph embeddings)
Build retrieval-augmented and tool-augmented systems (RAG, function/tool calling, agents) with robust evaluation
Create data pipelines for multimodal datasets (curation, augmentation, labeling) and enforce data quality standards
Implement benchmarking and evaluation frameworks (task metrics, bias/robustness checks, human-in-the-loop reviews)
Develop interactive demos and internal tools using modern web stacks (React, Node.js) to showcase capabilities
Write clean, well-tested code; document methods and results; contribute to publications, tech reports, and research artifacts

Benefits

GE provides travel reimbursement and housing stipend for qualified interns.
GE Aerospace offers a great work environment, professional development, challenging careers, and competitive compensation.
GE Aerospace is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.

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