Oracle Health & AI is a new line of business focused on modernizing and automating healthcare through AI. The Senior Applied Scientist will play a key role in shaping the future of AI at Oracle, with an emphasis on Large Language Models (LLMs) and Generative AI, to deliver new Generative AI-powered solutions for healthcare and enterprise customers.
Requirements
- 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.
- 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.
Responsibilities
- 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.
- Develop production code and advocate for the best coding and engineering practices.
- 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.
- Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals.
Other
- 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.
- Lead and mentor both junior and senior applied scientists.
- Participate in project planning, review, and retrospective sessions.
