Engineer

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.   Why join Coupa?

• Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend.
• Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.
• Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. 

Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa. 
The Impact of a Principal Engineer, AI/ML Architecture at Coupa:

Coupa's AI platform powers production agents, conversational AI (Navi), customer-built agents (Agent Studio), and a suite of ML models for classification, fraud detection, and supplier intelligence. The Principal Engineer, AI/ML Architecture will define the technical direction for the next generation of these capabilities, designing how we train, evaluate, and serve models that are deeply tuned to enterprise spend management. Reporting to the Sr. Director, you will be hands-on in architecture and prototyping while guiding a growing team of ML and data engineers.

What You'll Do:

• Define the architecture for model training, evaluation, and serving across Coupa's AI platform.
• Evaluate and select model approaches (open-weight, commercial, and hybrid) against enterprise accuracy and cost requirements.
• Design evaluation frameworks that measure model quality on Coupa's specific task categories.
• Drive technical partnership evaluations with AI infrastructure and model providers.
• Architect training data pipelines, including synthetic data generation and quality validation.
• Design retrieval-augmented generation (RAG) systems that extend our existing RAG infrastructure with structured knowledge retrieval.
• Establish technical standards for model safety, tenant data isolation, and responsible AI.
• Write code, review PRs, and prototype approaches, especially in the early phases.