Senior Machine Learning Engineer (REMOTE)

SailPoint is the leader in identity security for the cloud enterprise, providing unmatched visibility into digital workforces. As a Senior Machine Learning Engineer, you will be responsible for designing and implementing machine learning models that enhance SailPoint's AI capabilities, ensuring they meet customer needs and integrate seamlessly into the company's ecosystem.

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, TensorFlow, 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
• 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