Software Engineer (Machine Learning - LLMs)

Lendbuzz • Boston, MA

1d • $125,000 - $160,000 • Hybrid

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 a skilled and motivated Software Engineer focused on Large Language Model (LLM) applications to join our Machine Learning team. In this role, you will help design, build, and optimize next-generation conversational agent technologies. You will collaborate closely with ML researchers and product teams to ship high-impact features and own key components of our conversational AI stack. This position reports to the ML Research Scientist.

This is a hybrid position based in Boston, MA and requires 3 days onsite.

Requirements

• Master’s degree in Artificial Intelligence, Computer Science, or a related technical field
• Strong programming skills in Python, with experience in ML and data tooling (e.g., PyTorch, Pandas, NumPy, Scikit-learn)
• Experience with NLP techniques, LLMs, or machine learning fundamentals
• Strong problem-solving ability and comfort working independently in a fast-moving environment

Nice To Haves

• 2+ years of professional software engineering experience, including scripting, data processing, or backend/ML pipelines
• Experience deploying applications or models on cloud platforms, preferably AWS
• experience with real-time systems, WebSockets/streaming, RAG pipelines, vector databases, or ML evaluation frameworks, Genesys/Twilio

Responsibilities

• Software engineering for LLM-powered conversational agents, with an emphasis on practical implementation, reliability, and user experience
• Evaluate, fine-tune, and deploy LLM-based models and pipelines using REST APIs and internal microservices
• Implement prompt engineering, retrieval-augmented generation (RAG), tool-use pipelines, and conversation orchestration logic
• Investigate and integrate emerging technologies, particularly in real-time voice, streaming, and multi-modal interaction
• Analyze model outputs, user interactions, and system performance to drive iterative improvements
• Build and maintain high-quality datasets, including data cleaning, preprocessing, labeling workflows, and benchmarking for NLP tasks
• Own data quality, ensuring accuracy, reproducibility, and reliability across the data lifecycle
• Collaborate with ML, backend, and product teams on deployment best practices, monitoring, and scalability of LLM-based services
• Contribute to internal documentation, experimentation processes, and model evaluation frameworks

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level