Member of the Technical Staff — AI/ML

Stuut is transforming how B2B companies turn revenue into cash. Businesses worldwide have trillions tied up in receivables, yet the work between order and payment still relies on fragmented systems and labor-intensive manual processes. Stuut’s AI agent works across order management, credit, collections, payments, cash application, disputes and deductions, carrying context through every step. Customers reduce DSO by 47%, collect 40% more cash and eliminate 70% of manual work without replacing their ERP.

Stuut is already trusted by finance teams at companies including Honeywell, NCR and ZoomInfo, from Fortune 10 enterprises to scaling mid-market businesses. We are backed by a16z, Khosla Ventures, Activant, 1984 Ventures, Page One and Microsoft.

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

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

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

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

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

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

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

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

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

• 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