Software Engineer, AI
Ambrook’s mission is to help family-run American businesses become more profitable and resilient.
Operators across American agriculture and industry face increasing pressure from record-breaking droughts, rising input costs, and unpredictable markets. The best long-term investments, like efficient irrigation and grazing rotations, support both the land and the bottom line. But even when the payoff is clear, these changes need upfront capital and financial clarity that's hard to come by.
Business owners work with fragmented records and outdated tools. They can't easily see what's working or prove viability to a lender, partner, or the next generation.
Ambrook is rebuilding the financial infrastructure that independent operators rely on.
We replace paperwork and legacy systems with modern tools for accounting, banking, invoicing and spending. Tools built for people who spend more time in the field than in the office. Our platform gives producers the financial clarity they need to make confident investments in their land, their operation, and their future.
Our customers are the backbone of the real economy. They are stewards of land, labor, and legacy. We're giving them the ability to invest in stronger, more durable businesses. When they do, they build generational resilience across America.
We started with farmers and ranchers across the country. Now we're expanding quickly to other American industries.
We're a Series A startup backed by top investors like Thrive Capital, Dylan Field, Homebrew, Designer Fund, and BoxGroup. We're looking for early team members who want to untangle the knotted intersection between American industry, climate, and the economy.
The opportunity
Ambrook is building a world-class team to empower industrial businesses using modern AI tools. As one of our first engineers focused on AI and machine learning, you’ll help lay the foundation for how we use LLMs and intelligent systems to make messy, high-stakes financial workflows faster, smarter, and more accessible for farmers and ranchers.
We’re dealing with problems like the messy inbox problem, cleaning and categorizing transactions, summarizing financial documents, and surfacing the right insights at the right time — and we need an engineer who’s excited to build AI-powered tools that actually work in the wild.
We’re looking for someone who we can count on to…
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.
Within 1 month you'll...
Build and ship a feature powered by an LLM - e.g., parsing a new type of paper document, or categorizing financial transactions.
Review our current uses of AI and write up a note identifying where AI could meaningfully improve automation and accuracy.
Collaborate with product and engineering to understand customer workflows and where AI would reduce manual pain.
Within 3 months you'll...
Establish early foundations for evaluation pipelines, prompt management, and feedback loops to improve LLM performance over time.
Implement or improve systems for document extraction and financial transaction tagging, using both heuristics and model-based approaches.
Collaborate closely with design and product to make AI features feel reliable, intuitive, and helpful, not mysterious or brittle.
Within 6 months you'll...
Architect and scale systems for automated bookkeeping and analysis using hybrid AI + rule-based systems.
Contribute to our long-term AI strategy: which tasks to automate, how to measure confidence, and when to keep a human in the loop.
Help build internal tooling for prompt iteration, embedding search, labeling, and retraining.
Share learnings in a post on Ambrook Research or an open-source tool that helps others tackle similar messy real-world data problems.
About you
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.
Bonus: Experience working with LLM APIs, prompt engineering, embeddings, vector search, or fine-tuning.
Bonus: You've worked on products involving transactions, finance, document processing, or communications tooling.
Bonus: Experience with Typescript, React, and Python-based ML tooling (LangChain, OpenAI SDK, Pinecone, etc.).
Our Tech Stack
Next.js / React application written in Typescript
Hosted on Google Cloud
Firestore & Google Cloud Storage for data storage
BigQuery & Data Studio for data insights
Values
Real Talk – We create space for ourselves and others to be straightforward, vulnerable, and accountable.
Reach Understanding – We are driven by curiosity and empathy to learn about our customers, team, and world.
Be Proactively Resourceful – We are internally motivated and externally empowered to identify opportunities and solve problems.
Derisk Thoughtfully – We lean into the biggest risks we face as a company and put in the work to address them systematically.
Find the Positive-Sum – We believe in creating incentive structures that align the needs of our company, our customers, and our planet.
Ambrook is an equal opportunity employer. We are committed to building diversity and inclusion into our core company culture.
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