Principal Machine Learning Engineer
Ping Identity Corporation is an active enterprise identity-security company offering the Ping Identity Platform for customer, B2B, workforce, and AI-agent identity use cases.
Funding history
Investors
Projects
About Ping Identity Corporation
Ping Identity develops and operates identity and access management and identity-security products, including identity verification, identity management, authentication, authorization, threat protection, fraud prevention, and orchestration. Its offerings support multi-tenant SaaS, dedicated-tenant SaaS, self-managed software, and FedRAMP High deployments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will architect, build, and operate scalable machine-learning platforms, tools, and services. You will develop data and feature pipelines, enable low-latency model serving, automate model lifecycles, and guide technical architecture while mentoring engineers.
Requirements
- 6+ years of experience as an ML Engineer building ML platforms, data infrastructure, and large-scale distributed systems.
- Expert programming skills in Python and SQL.
- Experience with Spark or similar distributed-computing frameworks.
- Production experience with Airflow, MLflow, Kubernetes, and CI/CD pipelines.
- Experience with offline and online feature stores, real-time ML streaming data, and low-latency model serving.
- Experience with public cloud platforms, preferably AWS, including managed data and ML services.
- Experience developing monitoring tools and utilities for data scientists.
- Strong interpersonal communication, collaboration, and problem-solving skills.
Responsibilities
- Architect, build, and operate scalable ML platforms, tools, and services.
- Design and operate feature stores with offline and online consistency, reuse, lineage, and governance.
- Build streaming feature pipelines and low-latency model-serving systems.
- Build automated data pipelines for ML training, inference, and analytics.
- Drive end-to-end MLOps, including CI/CD, containerized deployment, monitoring, and model lifecycle management.
- Create tools and utilities that streamline data-science workflows.
- Architect and manage distributed big-data pipelines.
- Integrate ML capabilities with Data Science, SRE, and Product teams.
- Shape system architecture and technical direction, mentor engineers, and raise engineering standards.
Benefits
- Generous PTO and holiday schedule
- Parental leave
- Progressive healthcare options
- Retirement programs
- Education reimbursement
- Commuter offset at specific locations