Principal Applied Scientist

At Oracle Analytics, we are building the next generation of enterprise AI products to enable intelligent data analysis at scale. Leveraging our foundational strengths in data management and enterprise software applications, we are advancing our platforms and applications by deeply embedding cutting-edge agentic AI, generative AI, and innovations in machine learning and optimization.

Our AI and Applied Science team is looking for an experienced, highly-motivated Principal Applied Scientist to work on foundational technology for our AI Data platform and intelligent applications. In
this role you will drive innovation by researching, architecting, and prototyping AI models and solutions that advance the state of the art and unlock new capabilities for extracting value from enterprise data. You will partner with teams of applied scientists, research engineers, and production engineers to deploy your solutions into products with a global customer reach.

This role requires a solid basis in agentic and generative AI, the ability to design novel solutions in response to business requirements, and demonstrated experience in transitioning science solutions into production. Deep understanding and hands-on experience in LLM training, post-training, and/or fine-tuning, LLM adaptation/ model alignment with reinforcement learning, agent development, orchestration, and model evaluation are a must. Experience with agent orchestration, agent routers and vector DBs and latest retrieval algorithms would be a strong fit. We are looking for candidates who flourish in a fast-paced startup setting, excel at translating ambiguity into clarity, and value end-to-end ownership.

• Collaborate with a team of applied scientists and research engineers to architect, develop, and evaluate innovative science solutions in response to business requirements.
• Write high quality code to power experiments and build models. Contribute to writing production model code.

• Collaborate with data engineers to provide guidelines for data collection and annotation.
• Collaborate closely with cross-functional teams to deliver solutions into various applications and products.

• Identify new opportunities for scientific exploration and evaluate emerging technologies.
• Maintain a deep understanding of industry trends and developments in your field.

• PhD in Computer Science, Mathematics, Statistics, Engineering, or a related field
• Work experience in AI/Generative AI/Machine Learning in an industry setting

• Publication record in first-tier journals or conferences (e.g. ACL, EMNLP, NAACL, ICLR, NeurIPS)
• Hands-on development experience and a history of transitioning science solutions into successfully deployed products

• Deep understanding of state-of-the-art AI models for structured and unstructured data and their engineering implementations