Principal Machine Learning Engineer

About the role

About Ping Identity:

At Ping Identity, we believe in making digital experiences both secure and seamless for all users, without compromise. We call this digital freedom. And it's not just something we provide our customers. It's something that inspires our company. People don't come here to join a culture that's built on digital freedom. They come to cultivate it.

Our intelligent, cloud identity platform lets people shop, work, bank, and interact wherever and however they want. Without friction. Without fear.

While protecting digital identities is at the core of our technology, protecting individual identities is at the core of our culture. We champion every identity. One of our core values, Respect Individuality, reminds us to celebrate differences so you are empowered to bring your authentic self to work.

We're headquartered in Denver, Colorado and we have offices and employees around the globe. We serve the largest, most demanding enterprises worldwide, including more than half of the Fortune 100. At Ping Identity, we're changing the way people and businesses think about cybersecurity, digital experiences, and identity and access management.

Join us on Ping Identity's AI & Data Platform team as a Principal ML Engineer where you will architect, build, and scale the ML platform that powers Ping's intelligent identity products, enabling data scientists and engineers to train, optimize, and deploy low-latency decisioning models that run in the critical path of billions of identities. This role requires a multidisciplinary approach, combining expertise in data engineering, software development, MLOps, and ML automation. You will develop scalable ML platforms, create tools and utilities to streamline workflows, and drive the adaptation that takes models from research to production without friction.

Meet Our Team:

We are an experienced team of engineers who are hard working and easy to get along with. We have each other’s backs and are constantly sharing knowledge and pitching in whatever is needed to make sure our team and product are successful. We thrive on shared goals and shared success. We have mature yet lightweight processes around feature planning, defect tracking, and workload balance.

Job Functions:

- Architect, build, and operate scalable ML platforms, tools, and services that enable data scientists and engineers to promote, evaluate, deploy, and monitor models in production

- Design and run the ML models’ feature store, ensuring offline/online consistency, feature reuse, lineage, and governance across training and serving paths.

- Own real-time ML in the critical path: streaming feature pipelines and low-latency, sub-second model serving in production

- Build efficient, automated data pipelines using Python, SQL, Airflow, and Spark to support ML training, inference, and analytics

- Drive MLOps end to end — CI/CD for models, containerized deployment on Docker/Kubernetes, monitoring, and continuous model lifecycle management