Principal Machine Learning Engineer

Описание

This role on Ping Identity's AI & Data Platform team involves architecting, building, and scaling the ML platform that powers intelligent identity products, enabling data scientists and engineers to train, optimize, and deploy low-latency decisioning models.

Responsibilities

• 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

Requirements

• 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

Nice to have

• 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
• M.Sc. in the applied science field

We offer

• A flexible, collaborative work environment
• Employee Resource Groups
• Regular company and team bonding events
• Competitive benefits and perks
• Global volunteering and community initiatives
• Generous PTO & Holiday Schedule
• Parental Leave
• Progressive Healthcare Options
• Retirement Programs
• Opportunity for Education Reimbursement
• Commuter Offset (Specific locations)
• Final compensation for this role will be determined by various factors, such as knowledge, skills, and abilities