Research Scientist Intern - PhD

Saviynt•Milpitas, CA

18h

About The Position

Saviynt’s AI-powered identity platform manages and governs human and non-human access to organizations’ applications, data, and business processes. The company’s internship program offers hands-on experience solving real-world problems at the intersection of AI, security, cloud infrastructure, and data. Interns work alongside experienced engineers, data scientists, and product teams on innovation and product development, including machine learning and data science projects and AI security work. The internship is scheduled to start May 24, 2027, and end August 13, 2027. Compensation is estimated at $30–$50 per hour; final compensation depends on factors such as location, skills, experience, training, and business needs. The role requires adherence to Saviynt’s information security and privacy policies and procedures, including annual security training. Saviynt describes its work environment as welcoming and positive, with challenging work and opportunities to learn and grow.

Requirements

Currently enrolled in or recently completed a PhD program at an accredited US institution in Computer Science, Data Science, Electrical Engineering, Machine Learning, Robotics, or a closely related quantitative discipline.
Strong foundation in deep learning and state-of-the-art AI techniques.
Practical experience researching, implementing, and optimizing models.
Advanced proficiency in Python.
Familiarity with modern AI coding tools or agents, such as GitHub Copilot, Cursor, or Claude.
Demonstrated ability to take ownership of complex research problems, work under tight constraints, and deliver measurable, high-performance implementations.
Adhere to Saviynt’s information security and privacy policies and procedures, including annual security training.

Nice To Haves

Hands-on experience with PyTorch, TensorFlow, or Scikit-learn.
Familiarity with cloud environments such as AWS, Azure, or GCP, or containerization tools such as Docker or Kubernetes.
Track record of optimizing models for production, such as reducing latency or memory footprint or improving throughput.
Scholarly publications at top-tier AI/ML conferences such as NeurIPS, ICML, CVPR, or ACL, or relevant open-source research contributions.
Ability to translate complex mathematical and technical AI concepts into simple, actionable insights for technical and non-technical stakeholders.

Responsibilities

Collaborate with technical 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 the internship.
Work on machine learning and data science projects involving predictive models, data pipelines, and analytics systems, or on AI security topics involving the safety, robustness, and responsible use of AI systems.