Lendbuzz
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.
Base pay range $150,000.00/yr - $190,000.00/yr
This position is a hybrid role based in Boston, MA, requiring 3 days onsite.
Key Responsibilities
Research leadership & technical direction.
Lead research and development for conversational AI, agentic AI, and multi‑step reasoning systems.
Explore and prototype novel architectures, algorithms, and interaction paradigms across text, voice, and multi‑modal interfaces.
Define technical roadmaps, research priorities, and model evaluation strategies for agent behaviors, tool‑use workflows, and context management.
Modeling & System Development
Develop agent frameworks including prompt architectures, retrieval systems (RAG), planning modules, and tool orchestration pipelines.
Build robust real‑time systems including voice streaming, low‑latency inference, and continuous context tracking.
Design, fine‑tune, and deploy LLM‑based and multi‑modal models using internal infrastructure, REST APIs, and microservices.
Run systematic experiments to evaluate model quality, reliability, reasoning, and user experience.
Analyze conversational data, agent behavior traces, and performance metrics to guide iterative improvements.
Develop benchmarking protocols, simulation environments, and evaluation tooling for agentic workflows.
Data & Infrastructure Ownership
Build and curate high‑quality datasets for training and evaluation, including preprocessing, labeling workflows, and data governance.
Ensure data accuracy, reproducibility, and rigor across the research lifecycle.
Collaborate with backend and ML infrastructure teams on scalable deployment, monitoring, and optimization of agentic systems.
Cross‑functional Collaboration
Partner with product teams to translate research into production features.
Work with engineering teams to ensure reliability, safety, and performance of deployed models.
Contribute to internal documentation, research reports, and knowledge‑sharing sessions.
Key Requirements
PhD in Artificial Intelligence, Computer Science, Machine Learning, or a related technical field.
Prior research experience with Machine Learning / AI.
Strong proficiency in Python, with experience in ML frameworks (e.g., PyTorch) and data tooling (NumPy, Pandas, Scikit‑learn).
Demonstrated experience in NLP, LLMs, conversational AI, or machine learning research.
Strong problem‑solving skills and ability to operate independently while driving complex research initiatives.
Experience designing experiments, evaluating models, and working with large datasets.
Preferred Qualifications
2+ years of research or applied ML experience in conversational agents, LLM systems, or real‑time AI applications.
Experience deploying models or research prototypes to cloud environments.
Expertise with RAG pipelines, vector databases, reasoning or planning modules, or agent tool‑use systems.
A Note on Recruiting Outreach
We’ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (e.g., @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.
Seniority level Not Applicable
Employment type Full-time
Job function Engineering and Information Technology Transportation, Logistics, Supply Chain and Storage
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Base pay range $150,000.00/yr - $190,000.00/yr
This position is a hybrid role based in Boston, MA, requiring 3 days onsite.
Key Responsibilities
Research leadership & technical direction.
Lead research and development for conversational AI, agentic AI, and multi‑step reasoning systems.
Explore and prototype novel architectures, algorithms, and interaction paradigms across text, voice, and multi‑modal interfaces.
Define technical roadmaps, research priorities, and model evaluation strategies for agent behaviors, tool‑use workflows, and context management.
Modeling & System Development
Develop agent frameworks including prompt architectures, retrieval systems (RAG), planning modules, and tool orchestration pipelines.
Build robust real‑time systems including voice streaming, low‑latency inference, and continuous context tracking.
Design, fine‑tune, and deploy LLM‑based and multi‑modal models using internal infrastructure, REST APIs, and microservices.
Run systematic experiments to evaluate model quality, reliability, reasoning, and user experience.
Analyze conversational data, agent behavior traces, and performance metrics to guide iterative improvements.
Develop benchmarking protocols, simulation environments, and evaluation tooling for agentic workflows.
Data & Infrastructure Ownership
Build and curate high‑quality datasets for training and evaluation, including preprocessing, labeling workflows, and data governance.
Ensure data accuracy, reproducibility, and rigor across the research lifecycle.
Collaborate with backend and ML infrastructure teams on scalable deployment, monitoring, and optimization of agentic systems.
Cross‑functional Collaboration
Partner with product teams to translate research into production features.
Work with engineering teams to ensure reliability, safety, and performance of deployed models.
Contribute to internal documentation, research reports, and knowledge‑sharing sessions.
Key Requirements
PhD in Artificial Intelligence, Computer Science, Machine Learning, or a related technical field.
Prior research experience with Machine Learning / AI.
Strong proficiency in Python, with experience in ML frameworks (e.g., PyTorch) and data tooling (NumPy, Pandas, Scikit‑learn).
Demonstrated experience in NLP, LLMs, conversational AI, or machine learning research.
Strong problem‑solving skills and ability to operate independently while driving complex research initiatives.
Experience designing experiments, evaluating models, and working with large datasets.
Preferred Qualifications
2+ years of research or applied ML experience in conversational agents, LLM systems, or real‑time AI applications.
Experience deploying models or research prototypes to cloud environments.
Expertise with RAG pipelines, vector databases, reasoning or planning modules, or agent tool‑use systems.
A Note on Recruiting Outreach
We’ve been made aware of individuals falsely claiming to represent Lendbuzz using lookalike email addresses (e.g., @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.
Seniority level Not Applicable
Employment type Full-time
Job function Engineering and Information Technology Transportation, Logistics, Supply Chain and Storage
#J-18808-Ljbffr