Oracle is looking to solve high-priority technical gaps in areas such as model safety, bias mitigation, explainability, and performance optimization for their AI/ML solutions. This role aims to increase customer trust and adoption through more robust and responsible AI systems, strengthen their IP portfolio and thought leadership, and directly impact revenue, customer satisfaction, and competitive advantage by ensuring AI systems are performant, responsible, and aligned with user needs.
Requirements
- Hands-on experience with LLMs including fine-tuning, evaluation, and prompt engineering
- Demonstrated expertise in building or evaluating Responsible AI systems (e.g., fairness, safety, interpretability)
- Proficiency in Python and ML/DL frameworks such as PyTorch or TensorFlow
- Strong understanding of model evaluation techniques and metrics related to bias, robustness, and toxicity
- Experience with RLHF (Reinforcement Learning from Human Feedback) or other alignment methods
- Experience working with model guardrails, safety filters, or content moderation systems
- Creative problem-solving skills with a rapid prototyping mindset
Responsibilities
- Conduct cutting-edge research and development in Responsible AI, including fairness, robustness, explainability, and safety for generative models
- Design and implement safeguards, red teaming pipelines, and bias mitigation strategies for LLMs and other foundation models
- Contribute to the fine-tuning and alignment of LLMs using techniques such as prompt engineering, instruction tuning, and RLHF/DPO.
- Define and implement rigorous evaluation protocols (e.g., bias audits, toxicity analysis, robustness benchmarks)
- Collaborate cross-functionally with product, policy, legal, and engineering teams to ensure Responsible AI principles are embedded throughout the model lifecycle
- Publish in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR) and represent the company in academic and industry forums
- Invent, implement and deploy state-of-the-art machine learning and/or specific domain industry algorithms and systems.
Other
- Ph.D. in Computer Science, Machine Learning, NLP, or a related field, with publications in top-tier AI/ML conferences or journals
- Collaborative attitude
- Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.
- May be eligible for bonus, equity, and compensation deferral.
- Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.
