Principal Applied Scientist, Generative Ai

**What we offer**
- Being part of one of the most visionary and mission-driven organizations in Oracle, collaborating with talented peers with diverse backgrounds worldwide.
- High visibility to senior leadership, as well as technical leaders and partners.
- Opportunity to build state-of-the-art technologies in large language models and Generative AI at scale.
- Close partnership with product managers and software engineers to deploy solutions into production in various business-critical scenarios.
- Building performance evaluations of Generative AI systems for continuous improvement and alignment with stakeholders' growing expectations.

**Core Responsibilities**
- Develop, implement, and optimize large language models and Generative AI technologies, including training/finetuning and computation optimizations.
- Collaborate with software engineers to deploy LLM and other Generative AI models into production environments.
- Stay up to date with the latest advancements in the field of Generative AI.
- Collaborate with cross-functional teams to drive the development and adoption of LLM and Generative AI solutions across various organizations in the company.
- Work directly with key customers and accompany them on their AI journey - understanding their requirements, help them envision and design the right solutions and work with their engineering and data science team to remove blockers and translate feedback into actionable items for individual service owners.
- Design and build solutions to help customers reach successful pilots, PoCs and production deployments with our Generative AI and Data Science technologies.
- Consolidate learnings from these strategic engagements and present to the inbound product management teams to improve their products and platforms.
- Be an enthusiastic, self-motivated, and great collaborator with a can-do attitude
- Be a product evangelist - engage directly with customers and partners, participate and present in external events and conferences, when required

**Qualifications**:

- Advanced degree in computer science, engineering, or a field related to deep learning and/or NLP.
- Strong knowledge of ML fundamentals - supervised vs unsupervised modeling, time series, highly unbalanced and noisy data sets, complex feature engineering, recommendation systems, using and optimizing gradient boosting models, NLP, deep learning on all kinds of unstructured data.
- 5+ (for Senior), 7+ (for Principal), 10+ (for Sr Principal) years of work experience including a minimum of 2-year experience in developing large-scale ML solutions, and in particular deep learning solutions in the NLP field.
- Proficiency with deep learning frameworks such as PyTorch or TensorFlow, and deep learning architectures, especially Transformers.
- Hands-on experience with distributed training of large language models.
- Strong development experience of deep learning modeling in Python.
- Familiarity with the latest advancements in LLM and Generative AI technologies.
- Familiarity with engineering best practices, including shared codebase, version control, containerization, etc.
- Passionate about being a builder and working with talented peers to solve hard problems at scale.
- Good communication skills to convey technical concepts in straightforward terms with product managers and various stakeholders.

**Preferred Skills**
- Publications in top-tier deep learning conferences or significant contributions to prominent deep learning repositories
- Industrial experience in system design, software development, and production deployment
- Excel in transforming ambiguous requirements into actionable plans with deep learning techniques for problem-solving.
- First-hand experience with deep reinforcement learning
- First-hand experience with the latest technologies in LLM and Generative AI such as parameter-efficient finetuning and instruction finetuning
- Familiarity with the latest advancements in computer vision and multimodal modeling such as CLIP, DALL-E2, and diffusion models
- Top-tier performance in prestigious deep learning leaderboards or large model-related competitions.
- Having an impressive set of GitHub projects or contributions to open-source technologies

**Additional Responsibilities**

Defines the bar for science practices, and helps teams achieve those goals. Identifies and mitigates risks across full set of systems, particularly at the intersection of business and engineering. Innovate AI and ML powered solutions (rich APIs, ML models and end to end services) with strategic ISVs and customers. Develop deep product intuition to influence future product roadmaps and drive decision making. Clearly articulate technical work to audiences of all levels and across multiple functional areas in both internal and external settings. Engage in forward looking research both internal and with academic institutions globally. Invent, implement, and deploy state-of-the-art machine learning and/or specific domain indus