Senior Machine Learning Engineer; REMOTE

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 Senior 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.

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.

About the 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.
• Drive technical execution to improve model accuracy, precision/recall balance, and generalization across customer datasets and regions.
• Contribute to defining technical best practices for ML engineering across the AI team and participate in architecture and design discussions.
• Partner with product and engineering teams to scope, prioritize, and deliver impactful AI features aligned with SailPoint’s business goals.
• Work cross-functionally with architecture, platform, and analytics teams to integrate ML systems seamlessly into SailPoint’s ecosystem.
• Champion responsible AI principles and support ongoing improvements in model governance, explainability, and fairness.
• Communicate technical insights clearly, enabling shared understanding across technical and non-technical stakeholders.

Requirements:

• 5+ 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).
• Solid understanding of data modeling, feature engineering, and statistical analysis.
• Hands-on experience with data pipelines and ETL frameworks such as Spark, Airflow, or dbt.
• Working knowledge of MLOps practices—model monitoring, retraining, CI/CD, and experiment tracking.
• Strong grasp of software engineering fundamentals: testing, modularization, code review, and observability.
• Excellent communication and collaboration skills; proven ability to work effectively across cross-functional teams.

Preferred

• Exposure to LLM-based solutions, embeddings, or retrieval-augmented generation (RAG).
• Understanding of identity, security, or enterprise SaaS systems.
• Experience contributing to or extending shared ML infrastructure or platform components.

Roadmap for success-

30 days:

• 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, Dev Ops, and product teams.
• Review current ML models, data flows, and monitoring systems to identify optimization opportunities.
• Contribute to initial improvements or…