This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description - Design, prototype, research, and build AI systems for the Company. - Train, evaluate, and deploy ML models in Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs), and Multimodal Large Models (MLMs). - Improve the quality of the Company's RAG-as-a-service platform, including areas such as multilinguality, self-supervised learning, agentic behavior, and hallucination reduction. - Publish technical blogs, research papers, and patents. Qualifications - BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. - 4+ years of experience after BS/MS. - Strong software engineering fundamentals; role involves research as well as writing production-grade code. - Knowledge of common challenges in training machine learning models and best-practice solutions. - Familiarity with deep learning concepts such as Transformers, Retrieval-Augmented Generation (RAG), and Mixture of Experts (MoE). - Proficiency in data/ML libraries such as pandas, transformers, and torch. - Hands-on experience training ML systems end-to-end, from data curation to evaluation and deployment. - Ability to collaborate effectively with cross-functional teams. - PhD in Computer Science/Engineering with 1+ years of industry experience (preferred). - Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. - Experience working as an ML engineer in an early-stage, high-growth environment. - Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25. - Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection. Requirements - BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. - 4+ years of experience after BS/MS. - Strong software engineering fundamentals; role involves research as well as writing production-grade code. - Knowledge of common challenges in training machine learning models and best-practice solutions. - Familiarity with deep learning concepts such as Transformers, Retrieval-Augmented Generation (RAG), and Mixture of Experts (MoE). - Proficiency in data/ML libraries such as pandas, transformers, and torch. - Hands-on experience training ML systems end-to-end, from data curation to evaluation and deployment. - Ability to collaborate effectively with cross-functional teams. - PhD in Computer Science/Engineering with 1+ years of industry experience (preferred). - Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. - Experience working as an ML engineer in an early-stage, high-growth environment. - Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25. - Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description - Design, prototype, research, and build AI systems for the Company. - Train, evaluate, and deploy ML models in Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs), and Multimodal Large Models (MLMs). - Improve the quality of the Company's AI Agents and RAG-as-a-service platform, including features such as agentic behavior, hallucination reduction/correction, and agent orchestration. - Publish technical blogs, research papers, and patents. Qualifications - BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. - 7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions. - Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs. - Excellent programming skills in Python. - Proficiency in data/ML libraries such as pandas, transformers, and torch. - Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE). - Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment. - PhD in Computer Science/Engineering with 1+ years of industry experience. - Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. - Experience as an ML engineer in an early-stage, high-growth environment. - Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25. - Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection. Requirements - BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. - 7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions. - Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs. - Excellent programming skills in Python. - Proficiency in data/ML libraries such as pandas, transformers, and torch. - Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE). - Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment. - PhD in Computer Science/Engineering with 1+ years of industry experience. - Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. - Experience as an ML engineer in an early-stage, high-growth environment. - Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25. - Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection.

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 team: The AI Group at SailPoint is seeking a Senior Machine Learning Engineer to help shape the future of AI-driven identity governance. In this role, you will contribute to the design and development of SailPoint’s AI Platform and leverage it to deliver innovative machine learning and generative AI solutions that power our core products. If you are passionate about creating scalable, impactful AI/ML systems and enjoy working at the intersection of cutting-edge research and product development, we would love to connect with you. This position is Remote or Hybrid (Austin, TX). Responsibilities - Design, build, and maintain a production-grade machine learning platform - Develop and deploy ML models using the platform - Extend platform capabilities to support Generative AI workloads - Establish repeatable patterns that ensure reliable model deployments - Collaborate with ML Engineers, Software Engineers, and Data Scientists to deliver production-ready AI features - Ensure models are deployed, monitored, and tracked with clear, actionable dashboards. - Deliver efficient, maintainable, and robust microservices to support AI features. - Partner with application teams to build secure, scalable, and SLA-compliant solutions - Build batch and streaming data pipelines to populate the feature store. - Must be willing to be part of an on-call rotation. Requirements - Minimum of 5+ years of experience working as a Machine Learning Engineer or Data Scientist. - Experience deploying ML models into production. - A self-starter who can engage on ambiguous assignments and provide thought leadership. - Strong verbal and written communication skills, with the ability to translate complex concepts into easy-to-understand language for product and business stakeholders. - Understanding of common ML algorithms and techniques (e.g., clustering, classification, regression). - Strong command of MLOps best practices and existing frameworks. - Experience with feature stores such as Feast. - Experience implementing RESTful APIs for an API-first application architecture. - Experience building microservices and knowledge of common microservice design patterns. - Experience with Docker or other container technologies. - (Preferred) Experience with AWS, Amazon SageMaker, and Amazon Bedrock. - (Preferred) Experience building services using GenAI technology. - (Preferred) Experience with Amazon Bedrock or other services for building GenAI applications. Roadmap To Success First 90 Days - Get onboarded to SailPoint’s AI platform architecture and tools - Contribute to the codebase: small PRs on microservices or model pipelines. - Deploy a simple ML model or change to an existing model through the platform. - Define model monitoring and observability standards for a new model. - Shadow and eventually join the on-call rotation. 6 Months - Work on deployment of an end-to-end ML use case into production. - Deliver efficient, maintainable, and robust code. - Fully participate in on-call rotation and drive process improvements. - Identify and implement improvements to the broader AI Platform. 1 Year - Collaborate with multiple teams to ship high-impact ML/GenAI features. - Help shape SailPoint’s broader AI Platform strategy to drive measurable improvements in velocity and quality of model delivery. - Contribute to evaluation and adoption of new tooling to enhance our platform. - Mentor junior engineers and AI Platform users. 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): $119,400 - $170,500 - $221,700Base 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.

• Model development • AI integration into existing systems • Optimization of ML pipelines • Applied generative AI (GenAI) use cases