Machine Learning Engineer Co-op

LendbuzzBoston, MA
3d$25 - $30Onsite

About The Position

At Lendbuzz, we believe financial opportunity should be more personalized and fair. We develop innovative technologies that provide underserved and overlooked borrowers with better access to credit. From our employees to our dealers, partners, and borrowers, we’ve built a company and a culture around a resolute belief in the promise and power of diversity. We value independent and critical thinking. We are seeking an enthusiastic Machine Learning Co-op to join our team, focusing on our sales and collection AI chatbot projects within our Language Understanding and Semantic Analysis group. Ideal candidates should be eager to explore the intricacies of the auto loan industry, and interested in building real-world applications of Large Language Models (LLMs). You will gain hands-on experience with large-scale data annotation, data cleaning, multilingual model evaluation, and product design

Requirements

  • Pursuing a Bachelor’s degree in Computer Science or a related field, pursuing a M.S in Computer Science or a related field preferred
  • Coursework or experience in Data Structures, Algorithms, Linear Algebra, Probability, and foundational Machine Learning and Artificial Intelligence concepts
  • Familiarity with Python and its data analysis libraries
  • Strong grasp of core CS and ML concepts
  • Interview process will include questions on coursework, coding, probability, machine learning, and Linux fundamentals
  • Able to work 40/hours a week from May-December in our Boston office ( 3 days/week)

Responsibilities

  • Annotate chat data from sales-dealers and customers to train AI chatbots
  • Develop and refine prompts and Retrieval-Augmented Generation (RAG) technique cvg cant es to enhance the performance of zero-shot and few-shot language models (LLMs)
  • Assess the needs of dealers and customers, and implement AI chatbot functionalities to address those needs
  • Fine-tune language models as time permits and based on the availability of annotated data
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