Seeking a Senior Machine Learning Engineer to drive innovation in Generative AI and data-centric methods. This role involves designing and building cutting-edge solutions for synthetic data generation, multimodal data curation, and enhancing data quality for AI applications.

You will be instrumental in developing and evaluating synthetic data pipelines across text, speech, and vision, ensuring controllable generation, safety, and domain adaptation. The position offers a unique opportunity to translate research advancements into practical, production-ready systems that significantly improve AI outcomes.

Join a team at the forefront of AI and cloud solutions, contributing to a future where technology empowers billions. Oracle fosters an inclusive environment where diverse perspectives are valued and every individual is empowered to contribute to groundbreaking innovations.

Lead the development and continuous enhancement of data-centric Generative AI solutions and synthetic data capabilities. Establish and uphold rigorous data quality standards, evaluation metrics, and validation processes for AI datasets and models.

Conduct comprehensive experiments, analyze findings, and propose data or modeling enhancements based on measurable performance. Collaborate closely with cross-functional teams to prioritize use cases, align on technical specifications, and deliver scalable production solutions.

Contribute to technical designs, experiment reports, documentation, and presentations for stakeholders. Provide crucial support for production deployments through robust monitoring, quality control, operational procedures, and troubleshooting. Stay abreast of the latest research and industry trends, applying valuable techniques to real-world business challenges.

A Ph.D., Master's degree, or equivalent practical experience in a related technical field such as Computer Science or Artificial Intelligence is required. Candidates should possess at least 3-5 years of relevant experience applying machine learning to real-world challenges.

Essential skills include strong Python programming proficiency and experience in building production-quality ML, GenAI, or data systems. Hands-on experience with PyTorch and modern deep-learning frameworks is crucial. Experience with data-centric AI methods like synthetic data generation, data augmentation, and weak supervision is highly valued.

Demonstrated ability to design experiments, interpret results statistically, and evaluate research papers for practical implementation is necessary. Strong communication skills, both written and verbal, are expected for preparing technical documentation and presentations. Experience building scalable data or ML pipelines using distributed compute and cloud technologies is also a key requirement.