As a Research Engineer –Machine Learning, you will conduct research in natural language processing, large language models (LLMs), and generative AI. You will work with scientists and engineers to build search, recommendation, knowledge extraction, and copilot systems that help GE Aerospace teams make better, faster decisions.

You will:

• Develop and implement NLP/LLM and generative AI algorithms to process aerospace-domain text and multimodal data (e.g., technical documents, manuals, logs, maintenance records, design reports).
• Build systems in areas such as:
  • Search and information retrieval over large technical corpora
  • Recommendation systems for documents, designs, or procedures
  • Entity and relation extraction, knowledge graph construction, and structured information extraction
  • Generative AI applications including question answering, summarization, content generation, and LLM-based copilots embedded in engineering and services workflows
• Design and implement retrieval-augmented generation (RAG) and other hybrid architectures that combine LLMs with domain knowledge, physics-based models, and enterprise data sources.
• Explore and evaluate Gen AI techniques such as prompt engineering, instruction tuning, domain adaptation, tool-augmented LLMs, and synthetic data generation for model training and evaluation.
• Design and run experiments, define evaluation metrics, and conduct thorough offline, human-in-the-loop, and user-centric evaluations for generative and retrieval-based systems.
• Integrate NLP/LLM/Gen AI models with physics-based reasoning and engineering workflows where appropriate, ensuring reliability, controllability, and traceability.
• Validate solutions in realistic environments and help mature them for safe deployment and transfer to GE Aerospace businesses.
• Document technologies and results through patent applications, technical reports, and publications.
• Collaborate with a multidisciplinary team and effectively communicate results, limitations, and recommendations to stakeholders, including technical leaders and business partners.

You will address challenges unique to the aerospace domain, including highly domain-specific vocabulary, heterogeneous and noisy text sources, data quality and labeling constraints, and integration of Gen AI solutions into safety-critical, audited, and regulated workflows with strict requirements on safety, robustness, and compliance.

Required Qualifications

• Master’s degree in Computer Science, Electrical, Electronics, Industrial, Mechanical, or related Engineering field with specialization in NLP, Machine Learning, AI, or Statistics, and 3+ years of relevant experience;OR PhD in a related field.
• Demonstrated experience in one or more of:
  • Large Language Models (LLMs) or foundation models
  • Generative AI applications (e.g., text generation, summarization, conversational systems, copilots)
  • Information retrieval, search, and ranking
  • Entity and relation extraction, semantic parsing, or structured information extraction
  • Zero- / few-shot learning, prompt engineering, or instruction tuning
• Proficiency in Python and modern ML/NLP frameworks (e.g., PyTorch, TensorFlow, JAX, Hugging Face, or similar).
• Experience implementing data pipelines and training/evaluation code for NLP and/or generative tasks.
• Knowledge and application of data analytics, optimization, estimation, or detection algorithms, especially where data-driven models complement physics-based understanding.
• Self-starter with the ability to work in ambiguous environments and strong communication and collaboration skills.

Desired Characteristics

• Hands-on experience training, fine-tuning, evaluating, and deploying LLMs and Gen AI models, including instruction-tuned, domain-adapted, or tool-augmented models.
• Experience building retrieval-augmented generation (RAG) systems, semantic search, or recommendation systems for large technical or enterprise document collections.
• Experience implementing guardrails, safety controls, and evaluation frameworks for generative AI in enterprise or safety-critical settings.
• Experience working with industrial or safety-critical domains (e.g., Aviation, Energy, Healthcare, Manufacturing) and understanding robustness, traceability, and compliance requirements.
• Track record of publications at leading AI/NLP/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, or related workshops) is a plus.
• Strong foundations in algorithm design, evaluation methodology, and scalable implementation, including deployment on GPU or constrained/on-prem environments.
• Experience with end-to-end AI and Gen AI product or internal tool development, from concept and prototyping to pilot and technology transfer.

At GE Aerospace, we have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen. Here, you will have the opportunity to work on really cool things with really smart and collaborative people. Together, we will mobilize a new era of growth in aerospace and defense. Where others stop, we accelerate.