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 Machine Learning Engineer on the Core AI / ML team, you will be a senior technical leader responsible for shaping, scaling, and operationalizing ML capabilities that power SailPoint’s product offerings. This is a hands‑on, end‑to‑end technical leadership role. You will design and build foundational ML systems and models, influence cross‑team architecture, and set engineering standards that are adopted across multiple product lines. You will work at the intersection of modeling, ML infrastructure, and production systems, partnering closely with product, platform, and engineering leaders. You are expected to operate at organization scale: leading the most complex ML initiatives, mentoring engineers, and driving long‑term technical strategy while still contributing directly to critical designs and implementations.
About the team:
The AI team at SailPoint applies AI and domain expertise to create solutions that solve real problems in identity security. We believe the path to success is through meaningful customer outcomes, and we leverage classical ML, Graph ML, and recent innovations in Generative AI to bring our solutions to SailPoint’s core product lines.
Responsibilities:
- Define and lead the architectural vision for core ML systems, services, and platforms used across SailPoint products.
- Design, develop, and deploy production‑grade ML models including behavioral and anomaly detection, semantic search and embeddings, similarity‑based systems, graph‑based models, and LLM‑based or hybrid solutions where appropriate.
- Translate research, experimentation, and prototypes into scalable, maintainable, and reusable production systems.
- Own end‑to‑end technical design and delivery for complex ML initiatives, from data pipelines and feature engineering through deployment, monitoring, and lifecycle management.
- Drive continuous improvements in model quality, robustness, generalization, and performance across diverse enterprise datasets.
- Set and evolve ML engineering standards spanning experimentation rigor, evaluation, deployment, observability, and governance.
- Partner with platform, data, and DevOps teams to ensure reliable data access, cost‑efficient compute usage, and high system availability.
- Collaborate closely with product and engineering leaders to define AI roadmaps, prioritize work, and deliver high‑impact customer capabilities.
- Influence architectural decisions across teams to ensure ML solutions are reusable, scalable, and aligned with long‑term platform strategy.
- Communicate complex ML concepts and technical decisions clearly to technical and non‑technical stakeholders, including senior leadership.
- Mentor engineers on ML system design, software craftsmanship, and best practices for building production AI systems.
- Act as a technical authority for the most challenging ML and AI platform problems.
Requirements:
- 12+ years of experience in machine learning engineering, software engineering, or a related technical field.
- Proven track record of architecting and delivering large‑scale, production ML systems with meaningful business impact.
- Deep hands‑on expertise with ML frameworks such as PyTorch, TensorFlow, or scikit‑learn.
- Strong foundation in data modeling, feature engineering, statistics, and experimental design.
- Extensive experience with MLOps practices, including monitoring, CI/CD, experiment tracking, and model lifecycle management.
- Excellent communication and collaboration skills, with demonstrated ability to lead and influence cross‑functional, senior‑level stakeholders.
- BS or MS in Computer Science or a related field, or equivalent professional experience.
Preferred:
- Experience in cybersecurity, identity, or enterprise SaaS systems.
- Deep expertise and a strong track record in at least one of our core modeling areas: NLP, Behavioral Modeling, Time Series or Graph ML.
- Proven track record of building and deploying ML models at production scale (cloud-native environments preferred).
- Demonstrated ability to set technical direction, influence architectural decisions, and guide organizational strategy.
- Experience designing reusable AI platforms or ML services that support multiple product lines.
- Core Programming: SQL, Python, Shell/Bash, Java or Rust
- Cloud Platform: AWS (SageMaker, Bedrock)
- Data: Snowflake, DBT, Kafka, Airflow, Feast
- CI/CD: Cloudbees, Jenkins
SailPoint is an equal opportunity employer and we welcome all qualified candidates to apply to join our team. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other category protected by applicable law.
Alternative methods of applying for employment are available to individuals unable to submit an application through this site because of a disability. Contact applicationassistance@sailpoint.com or mail to 11120 Four Points Dr, Suite 100, Austin, TX 78726, to discuss reasonable accommodations. NOTE: Any unsolicited resumes sent by candidates or agencies to this email will not be considered for current openings at SailPoint.
