Machine Learning Engineer; Remote

Position: Staff Machine Learning Engineer (Remote) About Sail Point SailPoint is the leader in identity security for the cloud enterprise. Our identity security solutions secure and enable thousands of companies worldwide, giving our customers unmatched visibility into the entirety of their digital workforce and ensuring that workers have the right access to do their job—no more and no less. Built on a foundation of AI and ML, our Identity Security Cloud Platform delivers the right level of access to the right identities and resources at the right time—matching the scale, velocity, and changing needs of today’s cloud-oriented, modern enterprise.

About the Role

As a Staff Machine Learning Engineer, you will play a critical role in shaping, building, and scaling SailPoint’s AI-powered capabilities. You’ll work at the intersection of AI innovation, software engineering, and platform architecture—designing robust, production-grade ML systems that deliver customer insights and intelligent automation across our identity platform. As a senior technical leader, you’ll partner closely with engineering, AI, and product teams to drive innovation, define our ML strategy, and mentor others in applying best practices for scalable, responsible AI. This is both a hands-on and strategic role. You will lead complex, end-to-end ML initiatives—from model design and experimentation to deployment, monitoring, and continuous improvement—while advancing the evolution of SailPoint’s AI platform, data pipelines, and model governance standards. The AI Team The AI team at SailPoint applies AI and domain expertise to create AI solutions that solve real problems in identity security. We believe the path to success is through meaningful customer outcomes, and we leverage classical ML as well as recent innovations in Generative AI and Graph ML to bring our solutions to SailPoint’s core product lines.

Responsibilities

• Design, implement, and optimize ML models (supervised, unsupervised, and LLM-based) that power both customer-facing and internal product capabilities.
• Translate AI research and experimental prototypes into scalable, maintainable production systems.
• Lead technical efforts to improve model accuracy, precision/recall trade-offs, and generalization across diverse regions and customer datasets.
• Build and enhance ML infrastructure and pipelines for feature extraction, model training, evaluation, deployment, and monitoring.
• Drive the technical strategy for reproducibility, model versioning, data lineage, and bolthires/CD automation in ML systems.
• Collaborate with AI platform and Dev Ops teams to ensure reliable data access, observability, and efficient use of compute resources.
• Set technical direction and best practices for ML engineering across the AI organization, influencing architecture and design standards.
• Mentor and guide engineers in scalable ML design patterns, experimentation frameworks, and software craftsmanship.
• Partner with product and engineering leaders to prioritize and deliver high-impact AI capabilities aligned with business goals.
• Work cross-functionally with architecture, platform, and analytics teams to ensure AI components integrate seamlessly across SailPoint’s ecosystem.
• Advance model lifecycle management, AI governance, and responsible AI practices to ensure quality, fairness, and transparency.
• Communicate complex ML concepts into actionable insights and recommendations for technical and non-technical audiences.
• Support day-to-day team operations in partnership with TPMs and managers, ensuring alignment and delivery across initiatives.

Requirements

• 8+ years of professional experience in machine learning engineering, software development, or a related technical field.
• Strong programming skills in Python and proficiency with ML frameworks such as PyTorch, Tensor Flow, or scikit-learn.
• Proven track record of building and deploying ML models at production scale (cloud-native environments preferred).
• Deep understanding of data modeling, feature engineering, and statistical analysis.
• Expertise in data pipelines, ETL, and feature engineering using frameworks like Spark, Airflow, or dbt.
• Solid knowledge of MLOps practices—including model monitoring, retraining, bolthires/CD, and…

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