Snr Director, Applied Science

Responsibilities

• Strategic Leadership: Set the long-range roadmap, and operating model for multimodal GenAI services and supporting infrastructure.
• Align investments with Oracle Cloud priorities, customer demand, model capability roadmaps, capacity plans, and executive business objectives.
• Multimodal GenAI Platform Direction: Lead development and delivery of capabilities spanning large language models, vision-language models, image and video understanding/generation, speech/audio, embeddings, retrieval-augmented generation, evaluation frameworks, safety systems, and model lifecycle tooling.
• Infrastructure and Capacity Management: Own planning and execution for the compute, networking, storage, data, inference/training at large scale, orchestration, observability, and GPU/HPC capacity required to support high-volume GenAI workloads.
• Drive disciplined capacity forecasting, utilization management, and cost controls.
• Applied Science and Engineering Execution: Lead applied scientists, engineers, data specialists, technical program managers, and infrastructure leaders in converting research advances into production-grade services with measurable business and customer impact.
• Reliability, Performance, and Operational Excellence: Establish service-level expectations, incident practices, change management, performance benchmarks, and operational dashboards for mission-critical GenAI platforms.
• Ensure services meet high standards for latency, throughput, availability, scalability, and security.
• Cross-Functional Collaboration: Partner with product management, engineering, OCI infrastructure, security, legal, finance, data operations, sales, and customer teams to define requirements, resolve dependencies, and deliver end-to-end GenAI solutions.
• Governance, Safety, and Responsible AI: Ensure GenAI systems are designed and operated with appropriate controls for data handling, model access, evaluation, safety, privacy, compliance, abuse prevention, auditability, and responsible AI practices.
• Customer and Business Impact: Translate strategic customer and internal Oracle needs into scalable platform capabilities.

Qualifications

• A Ph.D. or advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Data Science, Computer Engineering, or a related field is preferred; equivalent industry leadership experience will be considered.
• 15+ years of experience in technology leadership, applied science, AI/ML engineering, cloud infrastructure, distributed systems, or related domains, including significant experience leading large technical organizations.
• Proven track record delivering production AI, ML, or cloud infrastructure services at enterprise or hyperscale levels, with accountability for reliability, cost, performance, and customer adoption.
• Deep understanding of generative AI architectures, multimodal models, model serving, inference optimization, data pipelines, evaluation systems, RAG, prompt/model safety, and ML lifecycle management.
• Strong knowledge of cloud infrastructure, GPU/HPC capacity, networking, storage, observability, platform operations, security, and service management for high-scale workloads.
• Demonstrated ability to manage complex budgets, capacity plans, vendor dependencies, and executive-level trade-offs in environments with rapidly changing demand and technical requirements.
• Exceptional leadership skills, including hiring, developing, and leading senior managers, principal engineers, scientists, and cross-functional teams through ambiguity and high-pressure execution.
• Strong business acumen with the ability to connect technical strategy to customer outcomes, cost structure, risk management, and Oracle’s broader cloud and AI objectives.
• Excellent communication and executive presentation skills, with the ability to explain complex AI and infrastructure concepts to technical, business, finance, legal, and customer audiences.
• High judgment, ownership, and adaptability in a fast-paced environment with evolving priorities, large-scale dependencies, and mission-critical customer expectations.