GE Aerospace Research - Knowledge… at GE Aerospace · Niskayuna

Summary: GE Aerospace is seeking PhD student interns to advance research and prototyping in AI capabilities for the aerospace industry. The role involves contributing to research on multimodal reasoning, building prototypes, and collaborating with engineers to translate research into production-ready components. 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 Required Qualifications: - Currently enrollment in a PhD program in Computer Science, Electrical Engineering, AI/ML, or related field - Legal authorization to work in the U.S - Must be willing to work from the GE Aerospace Research office in Niskayuna, NY - Minimum GPA 3.0 / 4.0 scale Preferred Qualifications: - 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) - 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 Required Skills: LLMs, NLP, Python, AI/ML frameworks Important Skills: JavaScript, Knowledge graphs, Data pipelines, React, Node.js Nice-to-Have Skills: MLOps, Publications, Soft skills Benefits: Travel reimbursement, Housing stipend, Professional development, Challenging careers, Competitive compensation, Relocation Assistance Provided