GenAI PhD Applied Scientist

The Team

The Oracle Generative AI Service team operates with the agility of a startup, backed by the scale and customer focus of a leading enterprise software company. This team delivers innovative services at the intersection of artificial intelligence and cloud infrastructure, building and operating massive-scale cloud services utilizing state-of-the-art machine learning technologies. With Oracle Cloud Infrastructure experiencing an incredible 52% annual growth, you will contribute to a legacy of performance, reliability, and security, helping organizations worldwide tackle their most complex challenges.

You will join an outstanding team of researchers dedicated to advancing the state-of-the-art in areas such as Generative AI Applications, Agentic Platforms, Reasoning and Planning, Retrieval Augmented Generation, Generative AI Evaluation, Responsible AI, Models’ Verticalization, NL2Code, NL2SQL, Multimodal GenAI, and Speech.

Your Responsibilities

As an Applied Scientist, you will lead the development of distributed, scalable, high-performance AI model training and serving systems, collaborating closely with applied scientists and software engineers. Your role will involve deep dives into model structure to optimize performance and scalability, building cutting-edge systems in a rapidly evolving field. You will be pivotal in shaping the future of Generative AI at Oracle, with a strong focus on Large Language Models (LLMs) and Multi-modality Models, delivering new Generative AI-powered services for large-scale enterprise customers.

• Collaborate with product managers to translate business and product requirements into AI projects.
• Work with fellow technical leaders to ensure successful and timely delivery of models and service integration.
• Coordinate with global teams, driving projects from in-depth research to production-ready code.
• Develop new OCI services and features leveraging recent advances in generative AI, machine learning, and deep learning.
• Design and review generative AI architecture, including data, model, training, and evaluation, employing best practices.
• Lead and mentor both junior and senior machine learning engineers.
• Develop production code and advocate for best coding and engineering practices.
• Participate in project planning, review, and retrospective sessions.
• Identify and mitigate risks at the intersection of business and engineering.
• Work closely with cross-functional teams to integrate evaluation capabilities, identify new opportunities, and explore emerging technologies.

Qualifications

To succeed in this role, you should possess a PhD in Computer Science, Mathematics, Statistics, Physics, Linguistics, or a related field, with a dissertation, thesis, or final project focused on Machine Learning and Deep Learning, to be completed by December 2025. Strong programming skills in Python and experience with deep learning frameworks like TensorFlow or PyTorch are essential. A deep technical understanding of Machine Learning, Deep Learning architectures (such as Transformers), training methods, and optimizers is required, along with practical experience in the latest LLM and generative AI technologies, including parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering.

Hands-on experience with emerging LLM frameworks and plugins such as LangChain, LlamaIndex, VectorStores, Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, and Guidance is expected. A background in Natural Language Processing and/or Computer Vision, Machine Learning, and Software Development, coupled with research and development experience, collaboration, and innovation, is crucial. Experience in ASR, TTS, S2S translation, conversational AI, or related NLP topics is highly valued. You should demonstrate proven analytical and problem-solving abilities in AI/ML or speech/language processing domains, along with experience in designing data collection/annotation solutions and systematic evaluation for production systems. A commitment to staying updated with academic advances and applying them to solve business problems, combined with a strong publication record in top-tier journals or conferences, is also important. Candidates must reside in the United States and/or attend a US university and be able to obtain US work authorization in 2025.

Preferred Qualifications

• Knowledge of LLM and experience delivering Generative AI and Agent models.
• Familiarity and experience with the latest advancements in computer vision and multimodal modeling.
• Strong understanding of machine learning algorithms and architectures.
• Open-source contributions to the AI/ML community.
• Minimum 3.0 GPA.

Key skills/competency

• Generative AI
• Large Language Models (LLMs)
• Deep Learning
• Machine Learning
• Python Programming
• TensorFlow/PyTorch
• Distributed Systems
• NLP/Computer Vision
• AI Architecture
• Prompt Engineering