Principal Software Engineer

pingidentity; United States; Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

PTO & holidays

Parental leave

Healthcare options

Retirement programs

Education reimbursement

Commuter offset

Job summary

Ping Identity is seeking an experienced ML platform engineer to design, build, and operate the machine learning platform powering identity products. You will enable teams to train, evaluate, deploy, and monitor models at large scale with a focus on dependable feature pipelines and low-latency serving.

You will work across data infrastructure, software engineering, and MLOps, delivering scalable tools, streaming pipelines, and robust CI/CD practices.

Qualifications

• Six+ years building ML platforms and distributed systems.
• Strong Python and SQL skills; Java is a plus.
• Experience with Spark or a comparable distributed computing framework.
• Experience with Airflow, MLflow, Kubernetes, CI/CD pipelines.
• Experience with offline and online feature stores, streaming systems like Kafka or Flink, and low-latency model serving.
• Public cloud experience, preferably AWS.

Responsibilities

• Design and run scalable tools and services for model promotion, evaluation, deployment, and production monitoring.
• Develop feature-store capabilities that support reuse, governance, lineage, and consistency between offline training and online serving.
• Build streaming feature pipelines and real-time model-serving systems for latency-sensitive production use.
• Automate data workflows for training, inference, and analytics using Python, SQL, and distributed processing tools.
• Own model delivery practices such as CI/CD, containerized deployment, monitoring, and lifecycle management.
• Create utilities that improve the efficiency of model training and deployment for data scientists and engineers.

Skills

Python

SQL

Java

Tools

Spark

Airflow

MLflow

Kubernetes

Kafka

Flink

AWS

Job description

Role overview

Build and operate the machine learning platform behind identity products, enabling teams to train, evaluate, deploy, and monitor models used for real-time decisions at large scale. The work spans data infrastructure, software engineering, and MLOps, with a focus on dependable feature pipelines and low-latency model serving.

Responsibilities

• Design and run scalable tools and services for model promotion, evaluation, deployment, and production monitoring.


• Develop feature-store capabilities that support reuse, governance, lineage, and consistency between offline training and online serving.


• Build streaming feature pipelines and real-time model-serving systems for latency-sensitive production use.


• Automate data workflows for training, inference, and analytics using Python, SQL, and distributed processing tools.


• Own model delivery practices such as CI/CD, containerized deployment, monitoring, and lifecycle management.


• Create utilities that improve the efficiency of model training and deployment for data scientists and engineers.

Requirements

• At least six years building ML platforms, data infrastructure, and large-scale distributed systems.


• Expert programming ability in Python and SQL; Java is an advantage.


• Hands-on experience with Spark or a comparable distributed computing framework.


• Production experience with Airflow, MLflow, Kubernetes, and CI/CD pipelines.


• Experience with offline and online feature stores, streaming systems such as Kafka or Flink, and low-latency model serving.


• Public cloud experience, preferably AWS, including managed data or machine learning services; strong problem-solving and cross-team communication skills.

Nice to have

• Experience with lakehouse and data platform tools such as Apache Iceberg, dbt, or OpenMetadata, including data governance and lineage.


• Experience operating self-service ML infrastructure across multiple cloud accounts.


• Familiarity with deep learning, large language models, reinforcement learning, or PyTorch; a master’s degree in an applied science field.

Benefits and work setup

The source describes a hybrid arrangement in Tel Aviv.

Listed benefits include paid time off and holidays, parental leave, healthcare options, retirement programs, education reimbursement, and a commuter offset in some locations.

• paid time off and holidays


• parental leave


• healthcare options


• retirement programs


• education reimbursement


• a commuter offset in some locations.