Principal Machine Learning Engineer

Tel Aviv - Hybrid - Tel Aviv, Israel

We're headquartered in Denver, Colorado and we have offices and employees around the globe. We serve the largest, most demanding enterprises worldwide, including more than half of the Fortune 100. At Ping Identity, we're changing the way people and businesses think about cybersecurity, digital experiences, and identity and access management.

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

M.Sc. in the applied science field

Base Hiring Range:

In accordance with Colorado’s Equal Pay for Equal Work Act (SB 19-085) the approximate compensation range for this role in Colorado is listed above. Final compensation for this role will be determined by various factors, such as knowledge, skills, and abilities.