Senior Machine Learning Engineer

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 LearningEngineer, 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 GraphML 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 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.

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

• 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.

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 MLengineering 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 (if applicable):

• 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

Benefits and Compensation listed vary based on the location of your employment and the nature of your employment with SailPoint.

As a part of the total compensation package, this role may be eligible for the SailPoint Corporate Bonus Plan or a role-specific commission, along with potential eligibility for equity participation. SailPoint maintains broad salary ranges for its roles to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect SailPoint’s differing products, industries, and lines of business. Candidates are typically placed into the range based on the preceding factors as well as internal peer equity. We estimate the base salary, for US-based employees, will be in this range from (min-mid-max, USD):

Originally posted on Himalayas