About SailPoint
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 Sr. 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.
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, 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 ML engineering, 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.
Requirements
- 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.
Preferred Qualifications
- Experience in cybersecurity, identity, or enterprise SaaS systems.
- Expertise in at least one of our core modeling areas: NLP, Behavioral Modeling, or Graph ML.
- 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.
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, 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.
90 Days
- 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.
6 Months
- 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 ML engineering within the AI team.
1 Year
- Help establish a robust, scalable ML foundation across multiple AI initiatives.
- Deliver one or more high-impact ML solutions from concept to production.
- Mentor and elevate peers through collaboration and knowledge sharing.
The Tech Stack
- Core Programming: SQL, Python, Shell/Bash, Go
- Cloud Platform: AWS (SageMaker, Bedrock)
- Data: Snowflake, DBT, Kafka, Airflow, Feast
- Visualization: Tableau, Qlik
- CI/CD: Cloudbees, Jenkins