Principal Machine Learning Engineer
OracleAt Oracle Cloud Infrastructure (OCI), we build the future of the cloud for Enterprises as a diverse team of fellow creators and inventors. We act with the speed and attitude of a start-up, with the scale and customer-focus of the leading enterprise software company in the world.
In the OCI AI Science org we are addressing exciting challenges at the intersection of artificial intelligence and cutting-edge cloud infrastructure. As a Principal Machine Learning Engineer, you will lead the development of large-scale datasets and leverage machine learning and generative AI to advance Generative AI models. You will drive the design of benchmarking frameworks, define best practices for data quality and fairness, and partner with scientists, engineers, and product leaders to shape the org’s data strategy.
Responsibilities
Key Responsibilities:
- Lead the design, development, and scaling of large, high-quality datasets to advance generative AI models in multimodal domains (e.g., text, vision, speech).
- Define data standards and best practices for acquisition, cleaning, augmentation, annotation, and evaluation to ensure fairness, diversity, and representativeness.
- Guide the integration of cutting-edge techniques (e.g., fine-tuning, RLHF, domain adaptation) into data generation and model alignment pipelines.
- Provide technical leadership in building scalable, reliable data pipelines and synthetic data platforms for production environments.
- Evaluate and operationalize research innovations, shaping how data preparation and generative AI methods transition into production-ready solutions.
- Partner with research, engineering, and product leaders to define long-term data strategy and accelerate the adoption of generative AI solutions at scale.
- Mentor and provide thought leadership to scientists and engineers, fostering a culture of data excellence and innovation
Qualifications and Skills:
- Bachelors or Master’s in Computer Science, Data Science, AI/ML, or related field with 6+ years of industry experience.
- Proficiency in Python and solid foundation in applied ML methods.
- Proficiency with Pytorch, Torchvision, OpenCV, and similar, as well as building and deploying DNN models in production.
- Experience building large-scale data pipelines for acquisition, cleaning, augmentation, and validation.
- Ability to evaluate datasets for distribution, diversity, anomalies and fairness to assess overall quality and suitability for generative AI.
- Experience with Computer Vision, NLP, Transformers, Large Language Models, Generative AI, optimizations around LLM training and serving. Experience with Multimodal models a bonus.
- Familiarity with advanced techniques (e.g., RLHF, domain adaptation, data augmentation) and their application in generative AI workflows.
- Proven track record of delivering scalable, data-centric ML solutions.
- Excellent communication and leadership skills, with experience mentoring junior ML engineers and presenting technical strategies to senior stakeholders.
Qualifications
Career Level - IC4