Responsibilities
- Own: Development of intelligent systems and tools that use LLMs, NLP, and heuristics to streamline bookkeeping, inbox triage, and financial workflows.
- Teach: Best practices around ML system design, evaluation, prompt engineering, and scalable AI infrastructure.
- Learn: How messy real world financial data really is - from scanned PDFs to unformatted CSVs to text-heavy emails - and how to make sense of it using language models.
- Improve: Our internal tooling and infrastructure for classification, tagging, summarization, and user-in-the-loop AI systems.
Requirements
- You've built and shipped ML/AI-driven features, ideally with LLMs, in a product that’s in production, not just in a notebook.
- You're excited to apply language models to real-world, unstructured, often ugly datasets, and turn them into valuable product experiences.
- You're pragmatic about AI: you know when to use an LLM, when to write a regular expression, and when to ask the user.
Nice to Have
- Experience working with LLM APIs, prompt engineering, embeddings, vector search, or fine-tuning.
- You've worked on products involving transactions, finance, document processing, or communications tooling.
- Experience with Typescript, React, and Python-based ML tooling (LangChain, OpenAI SDK, Pinecone, etc.).
Benefits
- Competitive compensation
- Health insurance
- 401(k) with matching contribution
- Paid parental leave
- Flexible work hours and vacation time
- Work-from-home/remote office stipend
- Wellness stipend
- Professional development stipend
