Software Engineer (Machine Learning - LLMs)

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.

• 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

• 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)
• Preferred: 2+ years of professional software engineering experience, including scripting, data processing, or backend/ML pipelines
• Experience with NLP techniques, LLMs, or machine learning fundamentals
• Strong problem-solving ability and comfort working independently in a fast-moving environment
• Preferred: Experience deploying applications or models on cloud platforms, preferably AWS
• Bonus (not required): experience with real-time systems, WebSockets/streaming, RAG pipelines, vector databases, or ML evaluation frameworks, Genesys/Twilio

We believe:

Diversity is a competitive advantage. We celebrate our differences, and are better when we have a variety of experiences, viewpoints, and backgrounds.

Compassion is a strength. We care about our customers and look to build long-term relationships with them.

Simplicity is a key feature. We work hard to make our forms and processes as painless and intuitive as possible.

Honesty and transparency are non negotiable. We incorporate these traits in all of our interactions.

Financial opportunity belongs to everyone. We work every day to improve lives by extending this opportunity.

If you believe these things too then we would love to hear from you!

Background Checks

After an offer is accepted, Lendbuzz conducts a pre-employment background check. Any evaluation of the background check will be subject to an individualized assessment, taking into account the applicant’s or employee’s specific record and the responsibilities and requirements of the particular role.

A Note on Recruiting Outreach

We’ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (eg @lendbuzzcareers.com). Please note that all legitimate emails from our team come from @lendbuzz.com. We will never ask for sensitive information or conduct interviews via messaging apps.