• Design and build enterprise-grade AI/ML systems end-to-end — from data pipeline through model development, deployment, and production monitoring — given functional and non-functional requirements. • Develop an experimentation roadmap. • Set up a reproducible experimentation environment and maintain experimentation pipelines. • Monitor and maintain ML models in production to ensure optimal performance. • Develop robust, production-quality Python code and reusable software modules that power data processing workflows, ML pipelines, and AI-driven applications — going well beyond notebook-style scripting. • Leverage cloud-native data and ML services (AWS stack preferred: SageMaker, EMR, S3, Lambda, ECR). • Conduct technical discovery workshops with enterprise clients, contributing to solution architecture and proposal development with a full-stack technical perspective. • Lead technical delivery for major client initiatives. • Drive adoption of MLOps best practices and technical standards. • Mentor junior engineers and shape the team's technical direction. • Evaluate and champion new technologies and frameworks.

• Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics. • Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations. • Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations. • Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns. • Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering). • Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks. • Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments). • Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency. • Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services. • Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement. • Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices. • Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.

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 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. 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, 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. Requirements: - 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. Preferred - Experience with LLM-based solutions, embeddings, and retrieval-augmented generation (RAG). - Familiarity with identity, security, or enterprise SaaS systems. - Experience designing AI platforms or reusable ML services that support multiple product lines. - Demonstrated ability to set technical direction, influence architectural decisions, and guide organizational strategy. Roadmap for success- 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. 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): $141,300 - $238,124.00Base salaries for employees based in other locations are competitive for the employee’s home location. Benefits Overview 1. Health and wellness coverage: Medical, dental, and vision insurance 2. Disability coverage: Short-term and long-term disability 3. Life protection: Life insurance and Accidental Death & Dismemberment (AD&D) 4. Additional life coverage options: Supplemental life insurance for employees, spouses, and children 5. Flexible spending accounts for health care, and dependent care; limited purpose flexible spending account 6. Financial security: 401(k) Savings and Investment Plan with company matching 7. Time off benefits: Flexible vacation policy 8. Holidays: 8 paid holidays annually 9. Sick leave 10. Parental support: Paid parental leave 11. Employee Assistance Program (EAP) and Care Counselors 12. Voluntary benefits: Legal Assistance, Critical Illness, Accident, Hospital Indemnity and Pet Insurance options 13. Health Savings Account (HSA) with employer contribution 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.

• Lead the design and implementation of scalable, high-performance, and resilient ML solutions for AdTech use cases. • Architect and evolve the end-to-end machine learning pipeline – from data ingestion and training to real-time inference for our real-time bidding, targeting, and optimization algorithms. • Define the technical roadmap and vision for AI/ML in our platform, evaluating new tools and techniques (including the latest in deep learning and LLMs) and making strategic build-vs-buy decisions. • Develop intelligent systems using AI agents and agentic workflows to automate and optimize end-to-end campaign processes. • Partner with engineering, product, and data science teams to translate marketing objectives into ML-driven solutions. • Ensure system robustness and stability for ML services in a high-concurrency, low-latency environment. • Provide technical guidance and mentorship to other engineers and data scientists, fostering a culture of excellence in engineering and ML best practices.