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Senior AI Engineer, NLP & Training Data - 11316

Coupa·APAC·Bangalore, India, Flexible / Remote·senior
mlnlp
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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 Senior AI Engineer, NLP & Training Data at Coupa: Coupa already operates production ML models and frontier model integrations across its AI platform. The Sr. Engineer, AI / Machine Learning will own the model training and iteration workstream, building the fine-tuning pipelines, evaluation harnesses, and iterative training loops that take our model capabilities to the next level. Working closely with the Principal Architect, you will turn architecture decisions into production training infrastructure. What You'll Do Build and own the end-to-end model fine-tuning pipeline: data preprocessing, training, evaluation, and model registry. Implement and optimize fine-tuning techniques (QLoRA, LoRA, PEFT, full fine-tune) for our training workloads. Design and maintain evaluation harnesses with task-specific benchmarks and automated regression testing. Drive the training iteration loop: analyze results, diagnose failure modes, improve data and configuration. Implement e

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