Principal Engineer, AI/ML Architecture - 11310

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.

• 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.
• Mentor and guide ML and data engineers across US and India.
• Collaborate with product, existing AI platform, and cloud operations teams.

• 15+ years of software engineering experience, with 5+ years focused on ML/AI systems.
• Demonstrated experience training or fine-tuning large language models. Must have shipped a fine-tuned or domain-adapted model to production.
• Deep knowledge of transformer architectures, training optimization (LoRA, QLoRA, PEFT, RLHF, DPO), and inference serving.
• Experience with distributed training on GPU clusters.
• Strong understanding of RAG architectures, vector search, embedding models, and knowledge graph integration.
• Hands-on experience with cloud AI/ML services (model hosting, managed training, or equivalent).
• Experience designing and running custom evaluation suites for LLMs.
• Proficiency in Python, PyTorch, and ML infrastructure tooling.
• Advanced degree in Computer Science, Machine Learning, or equivalent practical experience.
• Experience with enterprise B2B SaaS platforms preferred.