AI​/ML Engineer

ABOUT KEEPER

We're a YC startup that combines great UX and heavy-lifting machine learning to deliver a whole new kind of tax filing experience.
We have:

• 40K paying users ($199 to $399 per year)
• 20K reviews averaging 4.8 stars on the App Store, Play Store, and Trust Pilot
• 20 full-time employees, $15M raised (Series A)

We're looking for an AI/ML engineer to build user-friendly features powered by cutting-edge tools. Here are some examples of projects we’re currently working on:

• Improving our state-of-the-art transformer-powered tax form parser
• Leveling up our industry-leading AI tax assistant
• Iterating on our core ML models: an NLP model to generate human-readable transaction descriptions, and a classification model to predict which transactions are tax-deductible

A successful applicant for this position will be:

• Deeply experienced in modern ML and AI systems, including OCR, RAG, LLM fine-tuning, prompt engineering, and AI agents
• Highly proficient in Python, with working knowledge of GCP infrastructure and modern ML tooling (e.g. Pandas, Hugging Face, Weights & Biases)
• Intensely product-minded, with a focus on translating models into thoughtful, borderline magical features
• Rapidly iterative, able to design smart tests, analyze outcomes, and refine features quickly
• Ownership-oriented, taking end-to-end responsibility for code quality, feature reliability, and production issues when they arise
• Collaborative and generous, willing and able to partner closely with cross‑functional teammates
• Passionate about the potential of AI to build great user experiences; experience in the tax domain is a plus but not required

We have a mix of remote and in-office employees. For this role, we are looking for someone who wants to work one or more days per week in our SF office (Financial District).

Applicants must be a U.S. citizen, green card holder, or H‑1B visa holder (transfer). We do not support F-1 OPT or STEM-OPT.
Other visa classifications (e.g., TN, E‑3, O‑1) may be considered on a case‑by‑case basis.