PhD Applied Scientist - Health & Analytics

Job Description Intended for students graduating with their Doctorate degree by, or have graduated within, 12 months of cohort start date in June/July 2026. Target

Start Date:

early-June or mid-July 2026

Our future success depends on hiring world-class, early in career talent who are looking to power next-generation services and solutions. We are hiring students with a diverse set of skills and experiences to work in and across teams. Our program is intended to enhance your overall learning experience, help build your network, and accelerate your opportunity to make an impact.

Oracle Health & Analytics is a new line of business that aims to leverage our expertise in IaaS and SaaS to transform the healthcare industry, provide patient‑centric care, and make the best clinical tools available around the world (). We are looking for the best and brightest technologists as we build the next generation of health platforms that will change the industry.

This is a greenfield effort with an entrepreneurial spirit that promotes an energetic and creative environment. We like to move fast and innovate, and we want your help to make it a world‑class engineering team that makes a significant impact.

Analytics Cloud is also a business platform that delivers a full range of analytic and reporting capabilities. Our Business Intelligence platform of products provides enterprise‑class level performance, security, scalability and manageability apart from the very sophisticated and innovative features in core analytics. The BI platform is world‑class federated database. It enables business applications to query data from multiple heterogeneous systems and perform advanced business analytics.

This platform enables companies to quickly and accurately gain a holistic view of their performance, as well as optimize their decision‑making process. We have successfully differentiated ourselves from the competition by providing unparalleled breadth and depth in our features.

Oracle is the world’s leading provider of business software. With a presence in over 175 countries, we are one of the biggest technology companies on the planet. We’re using innovative emerging technologies to tackle real‑world problems today. From advancing energy efficiency to reimagining online commerce, the work we do is not only transforming the world of business—it’s helping advance governments, power nonprofits, and giving billions of people the tools they need to outpace change.

For more information about Oracle (NYSE:

ORCL), visit us at (.html).

What You’ll Do:

As an Applied Scientist within Oracle Health and Analytics, you will be leading the effort of building distributed, scalable, high‑performance AI model training and serving systems in partnership with our applied scientists and software engineers. You will dive deep into model structure to optimize model performance and scalability. You will build state‑of‑the‑art systems with cutting‑edge technologies in this fast evolving area.

You will play a key role in shaping the future of Oracle, with an emphasis on AI and Machine Learning.

• PhD in Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning and Deep Learning by July 2026
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• Demonstrated experience in designing and implementing scalable AI models for production.
• Deep technical understanding of Machine Learning, Gen AI, Data Science, and Deep Learning architectures.
• Hands‑on experience with emerging LLM frameworks and plugins, such as Lang Chain, Llama Index, Vector Stores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc.
• Commitment to staying up‑to‑date with the field and applying academic advances to solve complex business problems, and bringing them into production.
• Strong publication record, including as a lead author or reviewer, in top‑tier journals or conferences.
• Reside in the United States and/or attend a university in the US.
• Able to obtain work authorization in the US in 2026
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• Familiarity and…