[All remote jobs](https://jobicy.com/jobs.md)[![Ping Identity logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/09/63e8d5a6-221.png)](https://jobicy.com/company/ping-identity.md)[Ping Identity](https://jobicy.com/company/ping-identity.md) # Principal Machine Learning Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/ping-identity.md)ShareRemote from[Israel](https://jobicy.com/job-region/israel.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished29 Oct 2026Apply before28 Nov 2026Listing views30Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Ping Identity is seeking a Principal Machine Learning Engineer to architect and operate a production ML platform for identity products. The role combines MLOps, data engineering, distributed systems, real-time feature pipelines, and low-latency model serving. The engineer will build tooling for training, deployment, monitoring, governance, and model lifecycle automation while partnering with Data Science, SRE, and Product. This is a senior technical leadership position requiring deep hands-on expertise with Python, SQL, Spark, Airflow, Kubernetes, Kafka, cloud services, and feature stores. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a principal-level platform role responsible for mission-critical, low-latency ML infrastructure at large scale. It requires broad technical depth across distributed data systems, cloud architecture, MLOps, and cross-functional technical leadership. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$205,000US market range (per year)$170k–$240k0$264k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No numeric employer compensation was disclosed. The annual figures are AI-estimated USD market compensation for a principal-level ML platform engineer and are not employer-provided pay. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning Engineering](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Engineering.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Apache Spark](https://jobicy.com/jobs?search_keywords=Apache%20Spark.md)[Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Kafka](https://jobicy.com/jobs?search_keywords=Kafka.md)[Feature Stores](https://jobicy.com/jobs?search_keywords=Feature%20Stores.md)[AWS SageMaker](https://jobicy.com/jobs?search_keywords=AWS%20SageMaker.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Model Serving](https://jobicy.com/jobs?search_keywords=Model%20Serving.md)[Data Pipelines](https://jobicy.com/jobs?search_keywords=Data%20Pipelines.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md) Sample interview questionsHow would you design an offline and online feature store that maintains consistency between training and serving?I would define versioned feature contracts and a shared transformation layer used by both batch and streaming paths. Offline data would support training and backfills, while the online store would serve low-latency values; I would add freshness checks, point-in-time correctness tests, lineage, and monitoring for training-serving skew. Describe how you would build a reliable real-time ML inference path with sub-second latency. I would use a streaming platform such as Kafka for event ingestion, perform deterministic feature transformations, and store online features in a low-latency system such as Redis. Model serving would run in containerized, autoscaled services with strict latency budgets, caching where appropriate, observability, fallbacks, and controlled model rollout mechanisms. What practices would you implement for CI/CD and lifecycle management of ML models? I would version code, data schemas, features, model artifacts, and deployment configurations; use automated validation gates for data quality, model performance, bias, and serving compatibility; and promote models through staged environments. Production monitoring would track latency, errors, feature drift, data quality, prediction distribution, and business outcomes, with rollback and retraining workflows. How have you approached scaling Spark-based data pipelines for ML workloads? I start by understanding partitioning, data skew, shuffle volume, storage layout, and workload SLAs. I use efficient file formats and table designs, tune executor and partition settings based on measurements, minimize unnecessary shuffles, and implement data-quality and idempotency controls so pipelines can be safely retried and operated at scale. How would you lead technical direction while remaining hands-on as a principal engineer? I would establish clear architectural principles, define measurable platform reliability and developer-experience goals, and turn ambiguous requirements into incremental technical roadmaps. I would stay close to critical design and implementation work, mentor engineers through reviews and pairing, and align Data Science, SRE, and Product stakeholders on trade-offs and delivery priorities. Opportunity details ## About this role. About Ping Identity: At Ping Identity, we believe in making digital experiences both secure and seamless for all users, without compromise. We call this digital freedom. And it’s not just something we provide our customers. It’s something that inspires our company. People don’t come here to join a culture that’s built on digital freedom. They come to cultivate it. Our intelligent, cloud identity platform lets people shop, work, bank, and interact wherever and however they want. Without friction. Without fear. While protecting digital identities is at the core of our technology, protecting individual identities is at the core of our culture. [We champion every identity.](https://www.pingidentity.com/en/company/championing-every-identity.html) One of our core values, Respect Individuality, reminds us to celebrate differences so you are empowered to bring your authentic self to work. 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. 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. Life at Ping: We believe in and facilitate a flexible, collaborative work environment. We’re growing quickly, but remain true to the innovative, can-do startup values that got us here. Most importantly, we keep hiring talented, smart, fun, and genuinely nice people because that’s who we want to succeed with every day. Here are just a few of the things that make Ping special: * A company culture that empowers you to do your best work. * Employee Resource Groups that create a sense of belonging for everyone. * Regular company and team bonding events. * Competitive benefits and perks. * Global volunteering and community initiatives Our Benefits: * Generous PTO & Holiday Schedule * Parental Leave * Progressive Healthcare Options * Retirement Programs * Opportunity for Education Reimbursement * Commuter Offset (Specific locations) Ping is the collective sum of all our individual experiences, backgrounds and influences and we pride ourselves in growing and learning together. We are committed to building an inclusive and diverse environment where everyone’s individuality is respected and everyone has an Identity. In recruiting for new colleagues, we welcome the unique contributions you can bring and encourage you to be your best self. We are an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex including sexual orientation and gender identity, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law. Show more [Apply now >](https://jobicy.com/jobs/157874-principal-machine-learning-engineer.md) > Annual salary information is not provided for this position. 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