SailPoint

Senior Machine Learning Engineer

Remote Machine Learning Engineer role with clear candidate location fit.

PostedJun 16, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

USA

Role overview

Readable role content extracted into sections for faster review.

Design, experiment with, and implement ML models to solve complex identity security challenges.
Take ownership of research and prototyping efforts in areas like embeddings, representation learning, and similarity measurement.
Translate AI research and prototypes into practical, effective, and production-ready systems.
Drive improvements in model accuracy, precision/recall, and generalization for your projects.
Implement and advocate for best practices in MLengineering, testing, and architecture.
Communicate complex ML concepts and project updates to technical and non-technical stakeholders.
Partner with product managers to scope and deliver high-impact AI capabilities.
Work cross-functionally with platform and analytics teams to ensure your components integrate seamlessly into SailPoint’s ecosystem.
Contribute to our model lifecycle management, AI governance, and responsible AI practices.

5+ years of professional experience in a technical field with a focus on machine learning.
Proven experience applying modeling techniques such as anomaly detection, semantic search, embeddings, or similarity measurement to real-world applications.
Strong programming skills in Python and proficiency with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
Solid understanding of data modeling, feature engineering, and statistical analysis.
Excellent communication skills and the ability to collaborate effectively in a cross-functional team.
Strong foundation in software engineering best practices: testing, modularization, code review, and observability.
Good knowledge of MLOps practices—including model monitoring, retraining, and CI/CD.

Experience in cybersecurity, identity, or enterprise SaaS systems.
Expertise in at least one of our core modeling areas: NLP, Behavioral Modeling, or GraphML.
Experience owning the technical design and delivery of complex ML components or features.
Hands-on experience building and deploying ML models in a cloud-native environment.

Build a strong understanding of SailPoint’s AI vision, architecture, and current ML initiatives.
Learn existing data pipelines, environments, and model deployment frameworks.
Establish working relationships with key partners across AI, platform, DevOps, and product teams.
Review current ML models, data flows, and monitoring systems to identify optimization opportunities.
Contribute to initial improvements or bug fixes to gain familiarity with production workflows.

Contribute to at least one end-to-end ML initiative or pilot, supporting improvements in performance, reliability, or scalability.
Participate in model evaluation and analysis, helping to identify gaps, edge cases, or areas for feature and data improvements to support robust production performance.
Collaborate with stakeholders to identify opportunities to improve scalability, reduce technical debt, or enhance ML capabilities.

Deliver a significant improvement to a core AI product’s performance, scalability, or reliability.
Contribute to the design or enhancement of a reusable ML component (e.g., inference service, feature store, or monitoring framework).
Be recognized as a key contributor and technical resource for MLengineering within the AI team.