• Dataset ownership: define specs; audit and curate large-scale audio/text; close corpus gaps and fix sample-level issues. • Quality instrumentation: build automated gates/metrics (e.g., SNR, clipping, VAD, WER, SV/LID, safety) with dashboards; validate against listening tests. • Classifiers and filters: train lightweight models to tag, score, and filter data (VAD, ASR gating, LID, SV/diarization, noise/safety); calibrate to subjective outcomes. • Cleaning and integrity: apply denoise/dereverb/de-clip when beneficial; deduplicate and decontaminate; prevent leakage; maintain lineage and versioned releases. • Data selection: optimize mixtures via sampling, weighting, curriculum, and active learning; mine hard negatives and long-tail cases. • Tooling and pipelines: ship reproducible ETL and validation; integrate quality gates into training/eval; add monitoring and alerts. • Human-in-the-loop and compliance: run MTurk/vendor annotation with strong QC; ensure consent/licensing/policy compliance; collaborate across teams and document datasets.

• Work with business and operational teams to understand the problem, the operational and system constraints, the available data, and decision points. • Define how opportunities fit within the long-term evolution of the systems. • Based on the problem and the available data, determine how we can measure success and monitor system outcomes/dynamics. • Define and drive the evolution of the solution, identifying points of high leverage, and phases/evolution of the system. • Evolve plans and direction as we iteratively gather feedback and learn about solution effectiveness. • Collaborate with cross-functional leaders to identify opportunities, communicate and evaluate solutions, and gather feedback. • Mentor of data scientists and analysts, fostering a collaborative and high-performing environment. • Provide technical guidance, set standards, and ensure the team uses appropriate tools and methodologies. • Stay current with emerging trends in data science, AI, and big data technologies to drive innovation and maintain a competitive advantage.

• Design, develop, and validate machine learning models for prediction, classification, segmentation, and optimization use cases. • Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation. • Apply statistical and machine learning techniques to extract insights from structured and semi-structured data. • Ensure models are interpretable, reproducible, and aligned with business objectives. • Build and maintain scalable ML pipelines for training, testing, deployment, and monitoring of models. • Deploy models into production environments using batch and real-time inference patterns. • Implement model versioning, monitoring, drift detection, and retraining strategies. • Partner with the Senior Data Engineer to leverage and enhance data pipelines for ML readiness. • Collaborate with the Senior Data Architect to align model development with enterprise data architecture and governance standards.

Role Description As a Principal 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. - Design and build foundational ML systems and models. - Influence cross-team architecture and set engineering standards adopted across multiple product lines. - Work at the intersection of modeling, ML infrastructure, and production systems. - Partner closely with product, platform, and engineering leaders. - Lead the most complex ML initiatives, mentor engineers, and drive long-term technical strategy. - Contribute directly to critical designs and implementations. 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 - LLM-based or hybrid solutions - 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. Qualifications - 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. Roadmap for Success - 30 days: - Develop a deep architectural understanding of SailPoint's identity platform, AI/ML infrastructure, and active initiatives. - Identify key strategic opportunities and technical challenges for integrating Core AI/ML capabilities. - Establish yourself as a technical leader within the team and build key relationships with stakeholders across engineering, AI, and product. - Begin contributing to architectural reviews and strategic discussions to gain familiarity with production practices. - 90 days: - Define and champion the architectural vision and roadmap for a major Core AI/ML initiative. - Lead the design and initial implementation of foundational components for that initiative. - Establish and evangelize best practices for ML engineering, MLOps, and responsible AI tailored to the identity domain. - 6 months: - Deliver significant, measurable impact on the performance, scalability, or capabilities of a core identity product through the integration of AI/ML. - Be recognized across the organization as the go-to technical expert for Core AI/ML and its application to identity security. - Drive key architectural decisions that shape the future of the AI platform and influence the broader engineering organization. - 1 year: - Solidify the technical foundation for Core AI/ML at SailPoint, ensuring it is robust, scalable, and a key driver of innovation. - Lead one or more flagship AI capabilities from strategic conception to successful production deployment, delivering transformative value to our customers. - Actively mentor and grow the next generation of technical leaders within the AI organization. Benefits - Health and wellness coverage: Medical, dental, and vision insurance - Disability coverage: Short-term and long-term disability - Life protection: Life insurance and Accidental Death & Dismemberment (AD&D) - Additional life coverage options: Supplemental life insurance for employees, spouses, and children - Flexible spending accounts for health care, and dependent care; limited purpose flexible spending account - Financial security: 401(k) Savings and Investment Plan with company matching - Time off benefits: Flexible vacation policy - Holidays: 8 paid holidays annually - Sick leave - Parental support: Paid parental leave - Employee Assistance Program (EAP) and Care Counselors - Voluntary benefits: Legal Assistance, Critical Illness, Accident, Hospital Indemnity and Pet Insurance options - Health Savings Account (HSA) with employer contribution