Member of the Technical Staff — AI/ML

The Role

We’re hiring a Member of Technical Staff – AI/ML to design, build, and deploy AI-powered systems that solve real-world financial operations challenges. You’ll take state-of-the-art AI research and translate it into production-grade features that deliver measurable customer impact. From intelligent invoice matching to automated payment reconciliation, you’ll create scalable, reliable AI applications that integrate seamlessly into our platform.

This is a hands-on role for an engineer who thrives at the intersection of AI innovation and practical business application — turning cutting-edge models into real-world value for mid-market CFOs

What You’ll Do

- Create production-ready AI applications and agentic systems that address customer financial workflow challenges

- Build tool-using LLM agents that surface insights, recommend next steps, and execute approved tasks — not just chatbots

- Refine capabilities like invoice matching, payment reconciliation, and financial document processing

- Apply and optimize LLMs and RAG systems for financial use cases, including fine-tuning on proprietary data where it moves the needle

- Build robust AI pipelines from ingestion to inference — reliable, maintainable, and cost-efficient through smart model routing

- Stand up golden datasets, agent tracing, regression-on-PR, and A/B testing so we ship confidently and catch silent regressions

- Partner with Product, Engineering, Data, and customers to translate business needs into AI solutions

- Treat every AI feature as a continuously-improving system — instrument everything, iterate

You Might Be a Fit If You…

- Have 5+ years of AI/ML experience

- Have shipped agentic products in production and understand the failure modes (tool use, planning, state, recovery, human-in-the-loop)

- Have integrated and fine-tuned LLMs, and built RAG systems for document- or data-intensive workflows

- Have trained and deployed classical ML models (risk scoring, forecasting, ranking, or similar) — feature engineering, model selection, evaluation, calibration

- Have strong opinions on AI/ML evals — golden datasets, offline + online evaluation, statistical significance, evals in CI

- Are familiar with LLM observability tooling (LangSmith, Braintrust, Arize, or similar) and treat tracing as table stakes

- Understand MLOps fundamentals: deployment, monitoring, A/B testing, model and prompt versioning, feature stores

- Are fluent in Python and modern AI/ML tooling (PyTorch, Transformers, scikit-learn, XGBoost, vLLM, LangChain/LlamaIndex, or equivalent)

- Have shipped AI/ML products that solved real business problems, not just prototypes

- Can translate business requirements into clear technical solutions

- Bonus: experience with model routing across providers

- Bonus: fintech, B2B SaaS, or AR/AP domain experience

- Are plugged into the AI/ML community and energized by bringing AI to real-world use cases