Job description

ABOUT STUUT Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises. Across order management, credit, collections, payments, cash application, disputes and deductions, Stuut does the work end to end and keeps going until the cash hits the bank. Over 150 companies, spanning the mid-market to the Fortune 50, including Honeywell, ZoomInfo and PerkinElmer, use Stuut to unlock up to 40% more cash flow and cut DSO by 47%. More than $3 billion has moved through the platform to date. Founded in New York by Tarek Alaruri and Ben Winter, Stuut has raised $93 million from Insight Partners, Andreessen Horowitz, M12, Microsoft’s Venture Fund, and others. Learn more at stuut.ai stuut.ai. 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 US Compensation - Top-of-market salary and equity package - Benefits (for U.S.-based full-time employees) - Medical, dental & vision insurance coverage for you - 401(k) & Match - Equity - Flexible PTO - Parental Leave Stuut is an equal opportunity employer committed to building a team with diverse backgrounds, perspectives, and experiences. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law. We are also committed to providing reasonable accommodations throughout the application and interview process. If you need any accommodations, please do not hesitate to reach out.

Requirements

Must-haves

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

Nice-to-haves

Bonus: experience with model routing across providers
Bonus: fintech, B2B SaaS, or AR/AP domain experience

Application prep

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Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave