Senior Principal Applied Scientist - AI Infrastructure Innovation

As a Senior Principal Applied Scientist on OCI’s AI Innovation team, you’ll shaping Oracle’s next generation AI infrastructure. You will bring foresight into concrete requirements for training and serving models at massive scale. Your evidence-backed insights will guide where we invest across the stack so OCI’s AI Infra offerings are ready for what is coming in the future.

This role requires a deep understanding of machine learning techniques, including foundation model evaluation, traditional NLP/CV metrics, human evaluation, confidence estimation, agentive application evaluation, and RAG application evaluation. The successful candidate will have experience in conducting in-depth research, producing production-ready code, and collaborating with cross-functional teams to integrate evaluation capabilities into various products. A strong background in programming languages, such as Python, and experience with machine learning frameworks, such as TensorFlow or PyTorch, is also required.

In this role, you will lead deep research and rigorous evaluation of LLMs and related systems, designing benchmarks that capture quality, latency, cost, and reliability in real production settings. Expect to prototype new training and inference methods and run large-scale experiments that reveal scaling behavior and bottlenecks.

A PhD in Computer Science, Mathematics, Statistics, Physics, Linguistics, or a related field, with a dissertation or thesis centered on machine learning techniques, is preferred, although a Master's or Bachelor's degree with relevant experience will also be considered.

Responsibilities

•    Research and Development: Conduct in-depth research on foundation model evaluation, including traditional NLP/CV metrics, LLMaaJ techniques, human evaluation, confidence estimation, agentive application evaluation, and RAG application evaluation. Produce production-ready code for handoff of POC applications to counterparts in Engineering.
•    Collaboration: Work closely with cross-functional teams to integrate evaluation capabilities into various applications and products.
•    Innovation: Identify new opportunities for evaluation and explore emerging technologies.
•    Stay Updated: Maintain a deep understanding of industry trends and advancements in evaluation.

Qualifications and Experience:
•    PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field (with a dissertation, thesis or final project centered in Machine Learning/Deep Learning/Generative AI/Computational Linguistics) and a minimum of 4 years work experience. Candidates without a PhD but with 5+ additional years' experience will be considered. 
•    Strong publication record, including as a lead author or reviewer, in top-tier journals or conferences.
•    Extensive experience in generative AI and model/application evaluation.
•    Strong understanding of machine learning algorithms and architectures
•    Excellent problem-solving and analytical skills
•    Strong leadership and communication abilities

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