Principal Machine Learning Engineer

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
• Create tools, and utilities that reduce overhead and let data scientists focus on model development and research
• Architect and manage distributed big-data pipelines (Spark, Kafka, cloud-native data platforms), ensuring high availability and performance
• Work closely with Data Science, SRE, and Product teams to integrate ML capabilities into Ping's ecosystem
• See the big picture while coding the details: shape system architecture and technical direction, mentor engineers, and raise the engineering bar across teams

Essential Qualifications:

• 6+ years of experience as an ML Engineer building ML platforms, data infrastructure, and large-scale distributed systems
• Expert-level programming skills in Python and SQL (Java is a plus)
• Deep hands-on experience with Spark or similar distributed computing frameworks
• Production experience with Airflow, MLflow, Kubernetes, and CI/CD pipelines
• Hands-on experience with offline and online feature stores (e.g., Redis) and real-time ML streaming data (e.g., Kafka and Flink) and low-latency model serving
• Experience with public cloud platforms (AWS preferred), including managed data and ML services (AWS SageMaker)
• Experience developing monitoring tools and utilities for data scientists, improving model training and deployment processes
• Ability to see the wide picture while implementing low-level technical details efficiently
• Great interpersonal communication skills, a strong team player who collaborates effectively across teams
• Strong problem-solving skills, thrives in fast-paced, agile environments

Desired Qualifications:

• Experience with modern lakehouse and data platform stacks, e.g., Apache Iceberg, dbt, OpenMetadata, and data governance/lineage
• Experience operating ML infrastructure across multi-account cloud environments with a self-service, low-ops philosophy
• Familiarity with deep learning, LLMs, or reinforcement learning, experience with PyTorch