Coupa already operates production ML models and frontier model integrations across its AI platform. The Sr. Lead Engineer, 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 experiment tracking, hyperparameter optimization, and reproducible training workflows.
- Collaborate on training data strategy with data engineering, including synthetic data generation.
- Evaluate model quality across safety, accuracy, latency, and cost dimensions.
- Contribute to model serving architecture and inference optimization.
- Mentor ML engineers across the team.
What You Will Bring to Coupa:
- 10+ years of software engineering experience, with 4+ years focused on ML/NLP systems.
- Hands-on experience fine-tuning large language models with parameter-efficient methods.
- Strong knowledge of transformer architectures, tokenization, and training optimization.
- Experience building production ML training pipelines with experiment tracking.
- Proficiency in Python, PyTorch, and distributed training frameworks.
- Experience with GPU-based training infrastructure in the cloud.
- Strong evaluation methodology: designing benchmarks, measuring quality, detecting regressions.
- Experience with RLHF, DPO, or other alignment techniques is a strong plus.
- BS/MS in Computer Science, Machine Learning, or equivalent experience.
Skills Required
- 10+ years of software engineering experience, with 4+ years focused on ML/NLP systems.
- Hands-on experience fine-tuning large language models with parameter-efficient methods (e.g., QLoRA, LoRA, PEFT).
- Strong knowledge of transformer architectures, tokenization, and training optimization.
- Experience building production ML training pipelines with experiment tracking and reproducible workflows.
- Proficiency in Python, PyTorch, and distributed training frameworks.
- Experience with GPU-based training infrastructure in the cloud.
- Strong evaluation methodology: designing benchmarks, measuring quality, detecting regressions.
- Experience with RLHF, DPO, or other alignment techniques.
- BS/MS in Computer Science, Machine Learning, or equivalent experience.
- Experience mentoring or leading ML engineers.
What the Team is Saying
What We Do
Coupa is a global technology company that helps businesses run smarter by connecting all the ways they spend money — from procurement and expenses to payments and supply chain decisions — in one intelligent platform. In simple terms, Coupa gives organizations the visibility and control they need to make better financial choices, reduce waste, and drive real impact. It’s where technology meets purpose: helping companies manage their resources more responsibly while creating a positive ripple across their people, partners, and the planet.
Why Work With Us
At Coupa, we prioritize an inclusive and empathetic workplace where every voice is valued. Our teams are proactive and accountable, ensuring we collaborate effectively to achieve our goals. The foundation of our culture rests on our people; we believe in fostering an environment that encourages innovation and curiosity.
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Employees engage in a combination of remote and on-site work.
Our virtual-first approach is intentional. It gives you the freedom to do your best work in a space that supports focus, balance, and creativity, while staying connected to a global team of changemakers who are redefining the future of business spend
