Principal Engineer, AI/ML Architecture - 11310

Coupa Software•Foster City, CA

1d•Hybrid

About The Position

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. 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.

Requirements

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.

Nice To Haves

Experience with enterprise B2B SaaS platforms preferred.

Responsibilities

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.