We are looking for highly motivated people with proven track record to conduct research in natural language processing, artificial intelligence, and machine learning. As a Research Intern, you will be working with scientists in GE Aerospace Research to develop search and recommendation systems to improve the productivity of our Engineering teams. Your responsibilities will include developing and implementing algorithms to process Aerospace domain data for recommendation systems, design experiments, conduct thorough evaluations, and document your work (e.g., publications, invention disclosures). It will also include effectively communicating your findings with the appropriate stakeholders. You will encounter and have an opportunity to tackle unique challenges posed by the data and problems in the Aerospace domain including data quality, highly domain specific vocabulary, and how to integrate NLP/LLM-based solutions into our safety critical and regulated workflows and processes.

Required Qualifications

• Enrolled in full-time B.Tech in Computer Science, Electronics, Industrial, Electrical, Mechanical or related Engineering field with specialization in Natural Language Processing, Machine Learning, AI or Statistics.
• Exposure and working knowledge in areas of Large Language Models, Entity and Relation extraction, Zero / Few-shot Learning.
• Proficient in implementing algorithms, data pipelines and solutions in Python.
• Self-starter, ability to work in ambiguous environments, and excellent communication skills.

Ideal Candidate:

Should have experience in data analytics, optimization, estimation, or detection algorithms.   

Desired Characteristics

• Currently enrolled in a B.Tech program
• Previous experience in training, fine-tuning (including instruction-tuning), and deploying Large Language Models.
• Proven track record of publications at top AI conferences (or co-located workshops).
• Experience of working with problems and data from industrial domains (Aviation, Energy, Healthcare, Manufacturing, etc.).
• Strong foundations in design, analysis, and implementation of algorithms in different computing architectures is desired.
• Experience with computer vision algorithms is a plus.