Senior Machine Learning Engineer (REMOTE)

📋 Description

• • As a Senior Machine Learning Engineer at SailPoint, you will be instrumental in advancing the company's AI-powered capabilities, operating at the critical nexus of cutting-edge AI innovation, robust software engineering, and scalable platform architecture.
• • Your primary responsibility will be to design, develop, and deploy sophisticated, production-grade Machine Learning (ML) systems that generate actionable customer insights and drive intelligent automation across SailPoint's comprehensive identity platform.
• • You will lead and manage complex, end-to-end ML initiatives, taking full ownership from the initial stages of model design and experimentation through to deployment, ongoing monitoring, and continuous refinement.
• • The AI team at SailPoint is dedicated to leveraging both classical ML techniques and recent advancements in Generative AI and Graph ML to solve real-world challenges within the realm of identity security, focusing on delivering meaningful customer outcomes.
• • You will be tasked with designing, experimenting with, and implementing ML models specifically tailored to address intricate identity security challenges, pushing the boundaries of what's possible in this domain.
• • A key aspect of your role will involve taking ownership of research and prototyping efforts in specialized areas such as embeddings, representation learning, and similarity measurement, contributing to the intellectual property and technical roadmap of the team.
• • You will be responsible for translating promising AI research findings and early-stage prototypes into practical, highly effective, and production-ready systems that can be reliably integrated into SailPoint's product offerings.
• • A significant focus will be placed on driving tangible improvements in model performance metrics, including accuracy, precision, recall, and generalization capabilities, ensuring the efficacy and robustness of the deployed models.
• • You will be a champion for and implementer of best practices in ML engineering, encompassing rigorous testing methodologies, modular code design, effective code reviews, and comprehensive observability strategies to ensure system health and maintainability.
• • Effective communication is paramount; you will be expected to articulate complex ML concepts, project progress, and technical findings clearly and concisely to a diverse audience, including both technical peers and non-technical stakeholders.
• • You will collaborate closely with product managers to meticulously scope out new AI capabilities and ensure their successful delivery, aligning technical development with product strategy and market needs.
• • Working cross-functionally with platform and analytics teams will be essential to guarantee that your ML components integrate seamlessly and efficiently within the broader SailPoint ecosystem, fostering a cohesive and high-performing technical environment.
• • You will actively contribute to the development and maintenance of our model lifecycle management processes, AI governance frameworks, and the implementation of responsible AI practices, ensuring ethical and secure AI deployment.
• • The role involves a structured approach to success, with defined milestones for the first 30, 90, 180 days, and one year, focusing on building foundational knowledge, contributing to key initiatives, delivering significant improvements, and establishing yourself as a technical leader.
• • Within the first 30 days, you will gain a deep understanding of SailPoint's AI vision, architecture, and ongoing ML projects, familiarize yourself with existing data pipelines, environments, and deployment frameworks, and establish crucial working relationships across various technical teams.
• • By the 90-day mark, you are expected to have contributed to at least one end-to-end ML initiative or pilot, actively participated in model evaluation and analysis to identify areas for improvement, and collaborated with stakeholders on enhancing scalability and reducing technical debt.
• • At the 6-month milestone, you should have delivered a notable improvement to a core AI product's performance, scalability, or reliability, and contributed to the design or enhancement of reusable ML components, solidifying your role as a key technical resource.
• • Within the first year, your objective is to help establish a robust and scalable ML foundation across multiple AI initiatives, deliver high-impact ML solutions from conception to production, and mentor and elevate your peers through knowledge sharing and collaborative efforts.
• • You will leverage a modern tech stack including Python, AWS (SageMaker, Bedrock), Snowflake, DBT, Kafka, Airflow, Feast, and CI/CD tools like Cloudbees and Jenkins to build and deploy these advanced ML solutions.