Principal Machine Learning Engineer

SailPoint is the leader in identity security for the cloud enterprise, providing unmatched visibility into the digital workforce. As a Principal Machine Learning Engineer, you will lead the architectural vision for core ML systems, design and deploy production-grade ML models, and mentor engineers while driving technical strategy.

Responsibilities:

• Define and lead the architectural vision for core ML systems, services, and platforms used across SailPoint products
• Design, develop, and deploy production‑grade ML models including behavioral and anomaly detection, semantic search and embeddings, similarity‑based systems, graph‑based models, and LLM‑based or hybrid solutions where appropriate
• Translate research, experimentation, and prototypes into scalable, maintainable, and reusable production systems
• Own end‑to‑end technical design and delivery for complex ML initiatives, from data pipelines and feature engineering through deployment, monitoring, and lifecycle management
• Drive continuous improvements in model quality, robustness, generalization, and performance across diverse enterprise datasets
• Set and evolve ML engineering standards spanning experimentation rigor, evaluation, deployment, observability, and governance
• Partner with platform, data, and DevOps teams to ensure reliable data access, cost‑efficient compute usage, and high system availability
• Collaborate closely with product and engineering leaders to define AI roadmaps, prioritize work, and deliver high‑impact customer capabilities
• Influence architectural decisions across teams to ensure ML solutions are reusable, scalable, and aligned with long‑term platform strategy
• Communicate complex ML concepts and technical decisions clearly to technical and non‑technical stakeholders, including senior leadership
• Mentor engineers on ML system design, software craftsmanship, and best practices for building production AI systems
• Act as a technical authority for the most challenging ML and AI platform problems

Requirements:

• 12+ years of experience in machine learning engineering, software engineering, or a related technical field
• Proven track record of architecting and delivering large-scale, production ML systems with meaningful business impact
• Deep hands-on expertise with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
• Strong foundation in data modeling, feature engineering, statistics, and experimental design
• Extensive experience with MLOps practices, including monitoring, CI/CD, experiment tracking, and model lifecycle management
• Excellent communication and collaboration skills, with demonstrated ability to lead and influence cross-functional, senior-level stakeholders
• BS or MS in Computer Science or a related field, or equivalent professional experience
• Experience in cybersecurity, identity, or enterprise SaaS systems
• Deep expertise and a strong track record in at least one of our core modeling areas: NLP, Behavioral Modeling, Time Series or Graph ML
• Proven track record of building and deploying ML models at production scale (cloud-native environments preferred)
• Demonstrated ability to set technical direction, influence architectural decisions, and guide organizational strategy
• Experience designing reusable AI platforms or ML services that support multiple product lines