Principal ML Engineer - Real-Time ML Platform Architect

Ping Identity; United States; Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Generous PTO & Holidays

Parental Leave

Progressive healthcare options

Retirement programs

Education reimbursement

Commuter offset

Job summary

Ping Identity is seeking a Principal ML Engineer to architect, build, and scale ML platforms powering intelligent identity products. You will enable data scientists and engineers to train, optimize, and deploy low-latency models in production.

You will work across data engineering, software development, and MLOps, creating scalable tools and governance for feature stores, pipelines, and model lifecycle management.

Qualifications

• 6+ years of experience building ML platforms and large-scale distributed systems.
• Proficiency in Python and SQL; Java is a plus.
• Hands-on with Spark, Airflow, Kubernetes, and CI/CD pipelines.

Responsibilities

• Architect, build and operate scalable ML platforms and services in production.
• Design feature stores with offline/online consistency and governance.
• Own real-time ML in the critical path and low-latency serving.
• Develop automated data pipelines using Python, SQL, Airflow and Spark.
• Drive MLOps end-to-end including CI/CD, Docker/Kubernetes deployment, and monitoring.
• Create tools to reduce overhead for data scientists and engineers.
• Collaborate with Data Science, SRE and Product teams to integrate ML capabilities.

Skills

ML engineering

Python

SQL

Spark

Kubernetes

CI/CD

Airflow

Kafka

Education

M.S.C. in applied science

Tools

Docker

MLflow

Redis

SageMaker

Job description

Ping Identity is seeking a Principal ML Engineer to architect, build, and scale ML platforms powering intelligent identity products. You will enable data scientists and engineers to train, optimize, and deploy low-latency models in production.

You will work across data engineering, software development, and MLOps, creating scalable tools and governance for feature stores, pipelines, and model lifecycle management.