Research Engineer (in accounting and NLP/AI) - BRC

The role involves implementing retrieval-augmented natural language inference models and LLMs, processing large-scale financial text data, and supporting empirical analysis of inconsistency scores.

• Drive the technical execution of the research project under the guidance of the PI and Co-PI, ensuring project deliverables and timelines are met.
• Design and implement the retrieval-augmented NLI pipeline for inconsistency and contradiction detection across corporate disclosures (earnings call transcripts and SEC filings).
• Perform large-scale text preprocessing, cleaning, segmentation, and indexing of earnings call transcripts and financial filing corpora.
• Implement claims extraction modules using LLM-based approaches to identify key assertions from corporate disclosures.
• Build semantic retrieval mechanisms (e.g., Sentence-BERT embedding similarity search) for matching claim-context pairs across disclosure channels.
• Implement and evaluate contradiction detection models using NLI frameworks and LLM prompting techniques.
• Aggregate model outputs into firm-quarter level scores and prepare structured datasets for empirical analysis.
• Support human-in-the-loop validation by coordinating with student annotators and preparing annotation guidelines.
• Maintain reproducible research workflows using version control (Git) and systematic documentation of processes, code, and findings.

• Bachelor’s degree or Master’s degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or a closely related field.
• Strong programming skills in Python, with experience in relevant libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn.
• Prior experience or coursework in natural language processing, machine learning, or information retrieval.
• Familiarity with version control tools (e.g., Git/GitHub) and collaborative development practices.
• Experience with financial data or financial text analysis will be advantageous.
• Familiarity with LLM APIs, prompt engineering, and retrieval-augmented generation (RAG) workflows will be advantageous.
• Good communication skills and ability to work both independently and collaboratively.