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Ambrook is a Series B fintech startup rebuilding financial infrastructure for American family-run businesses—farmers, ranchers, and small-business owners. The company helps these operators become more profitable and resilient by replacing legacy systems with modern tools for accounting, banking, and spending, enabling them to access capital and make confident long-term investments.
You will own the development of intelligent systems and tools that use LLMs, generative AI, and heuristics to streamline bookkeeping and financial workflows. Your work will directly power an "AI CFO" vision—supplementing customers' expertise with enterprise-grade financial intelligence. You'll be responsible for building systems that handle messy, real-world financial data (scanned PDFs, unformatted CSVs, text-heavy emails) and turning it into actionable insights.
Key responsibilities include:
- Developing and shipping AI-driven features that reduce user pain in critical bookkeeping workflows
- Designing and evaluating intelligent systems, including prompt engineering and scalable AI infrastructure
- Improving internal tooling for classification, tagging, summarization, and user-in-the-loop AI systems
- Collaborating with product, design, and operations to understand customer workflows and where AI adds value
- Scaling evaluation pipelines, document extraction systems, and financial transaction tagging (both heuristic and model-based)
- Contributing to long-term AI strategy: which tasks to automate, how to measure confidence, and when to keep humans in the loop
- Building internal tools for prompt iteration, embedding search, labeling, and retraining
Within your first month, you'll build an assistant powering a critical bookkeeping workflow and review current AI infrastructure. By month three, you'll implement improvements to document extraction and transaction tagging. By month six, you'll own and scale the backbone systems of the AI CFO vision.
The tech stack includes Next.js/React with TypeScript, AI SDK, Braintrust for evaluation, Google Cloud infrastructure, Firestore, BigQuery, and access to major LLM providers (Anthropic, OpenAI, Gemini).
REQUIREMENTS:
- Proven track record shipping ML/AI-driven features to production
- Experience with LLM APIs, prompt engineering, embeddings, vector search, or fine-tuning
- Pragmatic approach to AI: knowing when to use an LLM vs. regex vs. asking the user
- Understanding of experimentation in rapidly evolving AI landscapes
- Bonus: Experience with transactions, finance, document processing, or communications tooling
- Bonus: TypeScript proficiency and familiarity with AI tooling (Vercel AI SDK, Braintrust, provider APIs)
