Senior Applied Scientist - Oracle Health

Building off our Cloud momentum, Oracle has formed a new organization - Oracle Health & AI. This team will focus on product development and product strategy for Oracle Health, while building out a complete platform supporting modernized, automated healthcare. This is a net new line of business, constructed with an entrepreneurial spirit that promotes an energetic and creative environment. We are unencumbered and will need your contribution to make it a world class engineering center with the focus on excellence.

As our Senior Applied Scientist, you will play a key role in shaping the future of AI at Oracle, with an emphasis on Large Language Models (LLMs) and Generative AI. Your contributions will be pivotal in delivering our new Generative AI-powered solutions for healthcare and enterprise customers.

Responsibilities

• Collaborate with product managers to translate business and product requirements into AI projects.
• Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services.
• Coordinate with multinational teams to drive projects from research POC to production.
• Develop new healthcare and enterprise services and features leveraging recent advances in generative AI, machine learning and deep learning.
• Design and review the architecture of AI solutions, including data, model, training, and evaluation, employing best practices.
• Lead and mentor both junior and senior applied scientists.
• Develop production code and advocate for the best coding and engineering practices.
• Participate in project planning, review, and retrospective sessions.
Identify and mitigate risks in our plans and executions, especially at the intersection of business and engineering.

Qualifications and Experience

• Demonstrated experience in designing and implementing scalable AI models for production.
• Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers.
• Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts.
• Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc.
• Proven experience in designing data collection/annotation solutions and systematic evaluation necessary for developing and maintaining production systems.
• 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.
Experienced leading senior scientists and early-career scientists.

Preferred Qualifications

• Knowledge of healthcare and experience delivering healthcare AI models are a significant plus.
Familiarity and experience with the latest advancements in computer vision and multimodal modeling is a plus.

Education

• PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning and Deep Learning) with 3+ years relevant experience is preferred but not a must; OR
Masters or Bachelor’s in related field with 5+ years relevant experience

Oracle is committed to incorporating generative AI into our applications and workflows for business users. This role presents a unique opportunity to work on pioneering AI technologies and contribute to Oracle's Generative AI strategy. At Oracle, we value diversity and encourage continuous learning for career growth. Come join our team to make a significant difference!

Career Level - IC3

#LI-JS14

Identify data science use cases and design scalable solutions that can be built as a feature of the product/service. Contributes to writing production model code. Work with Software Engineering teams to deploy them in production. Set up environment needed to run experiments for all projects. Set up distributed environments. Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals. Design and execute offline/online experiments and model performance testing. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Address business/customer problems and questions using statistical and machine learning techniques to achieve business goals and KPI's. Come up with innovative solutions to address tradeoffs or challenges faced by team. Stay up-to date with research and trends regarding latest algorithms in ML or other industry/domain space. Perform research in emerging areas, which may include efficient neural network development including quantization, pruning, compression and neural architecture search, as well as novel differentiable compute primitives. May perform other duties as assigned.