Finance Natural Language Processing Machine Learning Engineer

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in natural language processing applied to financial data and systems. You will complete tasks at the intersection of NLP and finance — including model development, fine-tuning, and research tasks applied to earnings calls, financial filings, market sentiment, news analytics, and other text-rich financial data sources. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge finance NLP research, and a strong addition to your research portfolio.

Responsibilities

• Apply NLP and large language model techniques to financial data including SEC filings, earnings transcripts, analyst reports, and financial news
• Build and fine-tune models for financial sentiment analysis, event detection, named entity recognition, and information extraction
• Develop predictive models linking textual signals to market movements, credit risk, or other financial outcomes
• Design and evaluate retrieval-augmented generation (RAG) pipelines for financial document question answering and summarization
• Document methodologies, experimental results, and technical approaches clearly and reproducibly

Requirements

• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., ACL, EMNLP, NAACL, NeurIPS, or equivalent)
• Master's or PhD in Computer Science, Computational Linguistics, Statistics, Finance, or a related quantitative field
• Demonstrated expertise in NLP and its application to financial data, markets, or economic text corpora
• Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred Qualifications

• Hands-on experience with financial NLP datasets and benchmarks (e.g., FinQA, FPB, ECTSum, EDGAR filings, or similar)
• Familiarity with finance-specific language models (e.g., FinBERT, BloombergGPT, FinGPT, or similar)
• Experience with alternative data sources such as social media sentiment, satellite data, or web scraping for financial signals
• Background in TA'ing or teaching NLP, machine learning, or quantitative finance courses

Why Apply

• Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges
• Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges
• Exceptional Pay – Project-based pay ranges from $150–$200/hour
• Portfolio Building – Gain experience applying NLP to frontier financial modeling and analytics problems
• Professional Growth – Sharpen your skills on varied, challenging financial text datasets and models