Research Scientist Intern - PhD

Join Saviynt's Internship Program and gain hands-on experience solving real-world problems at the intersection of AI, security, cloud infrastructure, and data. Our interns work alongside experienced engineers, data scientists, and product teams to drive innovation, learn cutting-edge technologies, and make an impact from day one.

Machine Learning / Data Science - building and evaluating predictive models, data pipelines, and analytics systems

AI Security - working on the safety, robustness, and responsible use of AI systems

Please note: Due to the high volume of applications, our review process may take some time. We appreciate your patience and interest in Saviynt’s internship program. Please note that only candidates selected to move forward in the process will be contacted.

WHAT YOU WILL DO

• Collaborate with technical teams and cross-functional teams on impactful projects
• Design, implement, and test software components or models
• Explore new tools, frameworks, and datasets to enhance product capabilities
• Communicate results and insights to technical and non-technical audiences
• Contribute to documentation, presentations, or demos at the end of your internship

WHAT YOU BRING

• Currently enrolled in or recently completed a PhD degree program in Computer Science, Data Science, Electrical Engineering, Machine Learning, Robotics, or a closely related quantitative discipline at an accredited US institution.
• Strong foundation in deep learning, state-of-the-art AI techniques, and practical experience researching, implementing, and optimizing models.
• Advanced proficiency in Python and familiarity with leveraging modern AI coding tools/agents (e.g., GitHub Copilot, Cursor, Claude) to accelerate development and execution.
• Demonstrated ability to take ownership of complex research problems, work under tight constraints, and deliver measurable, high-performance implementations.

Preferred Qualifications

• Hands-on experience with PyTorch, TensorFlow, or Scikit-learn, along with familiarity in cloud environments (AWS, Azure, GCP) or containerization (Docker/Kubernetes).
• Proven track record of optimizing models for production environments (e.g., latency reduction, memory footprint optimization, or throughput improvement).
• Scholarly publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL) or relevant open-source research contributions.
• Ability to clearly translate complex mathematical/technical AI concepts into simple, actionable insights for technical and non-technical stakeholders alike.