Principal Software Engineer

Ping Identity

. Architect, build, and operate scalable ML platforms, tools, and services for promoting, evaluating, deploying, and monitoring production models .

Posted 10/28/2026full-timeTel Aviv • IsraelLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and operating scalable ML platforms and tools, with a strong focus on MLOps, real-time ML, and automated data pipelines. Proficient in Python, SQL, and distributed computing frameworks, with experience in cloud-native data platforms and collaboration across cross-functional teams.

Highest-signal resume keywords

ML Platform ArchitectureMLOps ImplementationPython ProgrammingSpark FrameworkCI/CD Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLSparkAirflowKubernetesCI/CDKafkaMLflowReal-Time MLFeature Store

Soft Skills

Interpersonal CommunicationCollaborationProblem-SolvingAgile Environment Adaptability

Tools & Technologies

AWS SageMakerDockerApache IcebergDbtOpenMetadata

Certifications & Qualifications

M.Sc. in Applied Science

Industry Keywords

ML InfrastructureDistributed SystemsData GovernanceModel Lifecycle ManagementStreaming Data

Tech Stack

Tools & technologies

AirflowApacheAWSCloudDistributed SystemsDockerJavaKafkaKubernetesPythonPyTorchRedisSparkSQL

About the role

Key responsibilities & impact

Architect, build, and operate scalable ML platforms, tools, and services for promoting, evaluating, deploying, and monitoring production models
Design and run the models’ feature store, ensuring offline/online consistency, feature reuse, lineage, and governance
Own real-time ML in the critical path, including streaming feature pipelines and sub-second model serving
Build automated data pipelines using Python, SQL, Airflow, and Spark for ML training, inference, and analytics
Drive end-to-end MLOps, including model CI/CD, Docker/Kubernetes deployment, monitoring, and model lifecycle management
Create tools and utilities that reduce overhead for data scientists
Architect and manage distributed big-data pipelines using Spark, Kafka, and cloud-native data platforms
Collaborate with Data Science, SRE, and Product teams to integrate ML capabilities into Ping Identity’s ecosystem
Shape system architecture and technical direction, mentor engineers, and raise engineering standards across teams

Requirements

What you’ll need

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), real-time ML streaming data (e.g., Kafka and Flink), and low-latency model serving
Experience with public cloud platforms, including managed data and ML services such as AWS SageMaker (AWS preferred)
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 and ability to collaborate effectively across teams
Strong problem-solving skills; thrives in fast-paced, agile environments
Experience with modern lakehouse and data platform stacks such as Apache Iceberg, dbt, OpenMetadata, and data governance/lineage (desired)
Experience operating ML infrastructure across multi-account cloud environments with a self-service, low-ops philosophy (desired)
Familiarity with deep learning, LLMs, or reinforcement learning, and experience with PyTorch (desired)
M.Sc. in the applied science field (desired)

Benefits

Comp & perks

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)