Staff Machine Learning Engineer, Sailpoint

• 8+ 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)
• Deep understanding of data modeling, feature engineering, and statistical analysis
• Expertise in data pipelines, ETL, and feature engineering using frameworks like Spark, Airflow, or dbt
• Solid knowledge of MLOps practices—including model monitoring, retraining, CI/CD, and experiment tracking
• Strong foundation in software engineering best practices: testing, modularization, code review, and observability
• Excellent communication and collaboration skills, with demonstrated experience leading cross-functional technical initiatives

Desirable

• As a Staff 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
• As a senior technical leader, you’ll partner closely with engineering, AI, and product teams to drive innovation, define our ML strategy, and mentor others in applying best practices for scalable, responsible AI
• This is both a hands-on and strategic role
• 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
• 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
• Lead technical efforts to improve model accuracy, precision/recall trade-offs, and generalization across diverse regions and customer datasets
• Build and enhance ML infrastructure and pipelines for feature extraction, model training, evaluation, deployment, and monitoring
• Drive the technical strategy for reproducibility, model versioning, data lineage, and CI/CD automation in ML systems
• Collaborate with AI platform and DevOps teams to ensure reliable data access, observability, and efficient use of compute resources
• Set technical direction and best practices for ML engineering across the AI organization, influencing architecture and design standards
• Mentor and guide engineers in scalable ML design patterns, experimentation frameworks, and software craftsmanship
• Partner with product and engineering leaders to prioritize and deliver high-impact AI capabilities aligned with business goals
• Work cross-functionally with architecture, platform, and analytics teams to ensure AI components integrate seamlessly across SailPoint’s ecosystem
• Advance model lifecycle management, AI governance, and responsible AI practices to ensure quality, fairness, and transparency
• Communicate complex ML concepts into actionable insights and recommendations for technical and non-technical audiences
• Support day-to-day team operations in partnership with TPMs and managers, ensuring alignment and delivery across initiatives
• 30 days:
• Gain deep understanding of SailPoint’s AI vision, architecture, and active ML initiatives
• Familiarize with existing data pipelines, environments, and model deployment frameworks
• Build relationships with key stakeholders across AI, platform, DevOps, and product teams
• Conduct hands-on review of current ML models, data flows, and monitoring systems to identify immediate optimization or reliability gaps
• Begin contributing to small improvements or code reviews to gain familiarity with production practices
• 90 days:
• Lead at least one end-to-end ML enhancement or pilot
• Establish and document best practices for reproducibility, observability, and CI/CD for ML systems
• Mentor junior engineers and support team-wide code quality and experimentation standards
• Present a roadmap or proposal for scaling AI components or addressing key technical debt areas
• 6 months:
• Deliver measurable impact on model performance, reliability, or scalability for at least one core AI product
• Lead design and implementation of a shared ML service or reusable component (e.g., feature store, inference service, or monitoring framework)
• Be recognized as a technical go-to for complex ML engineering and architecture decisions
• 1 year:
• Establish SailPoint’s ML engineering foundation as robust, scalable, and production-ready across multiple AI initiatives
• Drive one or more flagship AI capabilities from prototype to production, with demonstrated business or customer impact
• Mentor and elevate other engineers, fostering a culture of technical excellence and continuous learning
• Influence long-term AI platform architecture and strategic investment areas as part of the broader AI leadership group