Snr Director, Applied Science

Oracle Oracle · Enterprise · United States

Lead a high-impact organization responsible for advancing Oracle’s multimodal GenAI strategy and ensuring the infrastructure, capacity, reliability, governance, and operating model needed to deliver those capabilities at scale. This includes setting the roadmap, leading development of multimodal GenAI platform capabilities, owning infrastructure and capacity management for large-scale GenAI workloads, and ensuring reliability, performance, and operational excellence. The role also involves cross-functional collaboration, governance, safety, responsible AI practices, customer and business impact, budget management, and talent leadership.

What you'd actually do

  1. Set the long-range roadmap, and operating model for multimodal GenAI services and supporting infrastructure.
  2. 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.
  3. 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.
  4. 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.
  5. Establish service-level expectations, incident practices, change management, performance benchmarks, and operational dashboards for mission-critical GenAI platforms.

Skills

Required

  • multimodal GenAI strategy
  • infrastructure management
  • capacity planning
  • reliability engineering
  • governance
  • operating model development
  • large language models
  • vision-language models
  • image and video understanding/generation
  • speech/audio processing
  • embeddings
  • retrieval-augmented generation
  • evaluation frameworks
  • safety systems
  • model lifecycle tooling
  • compute, networking, storage, data management
  • inference and training infrastructure
  • orchestration
  • observability
  • GPU/HPC capacity management
  • applied science leadership
  • engineering leadership
  • data specialist leadership
  • technical program management
  • infrastructure leadership
  • service-level expectation setting
  • incident management
  • change management
  • performance benchmarking
  • operational dashboards
  • cross-functional collaboration
  • product management partnership
  • OCI infrastructure partnership
  • security partnership
  • legal partnership
  • finance partnership
  • data operations partnership
  • sales partnership
  • customer team partnership
  • governance, safety, and responsible AI
  • data handling controls
  • model access controls
  • evaluation controls
  • privacy controls
  • compliance controls
  • abuse prevention
  • auditability
  • customer needs translation
  • budget management
  • vendor management
  • resource management
  • talent and organizational leadership
  • mentoring
  • team building
  • accountability
  • innovation culture
  • customer focus
  • market and technology trend analysis
  • GenAI advances
  • multimodal foundation models
  • AI infrastructure trends
  • inference optimization trends
  • GPU/accelerator trends
  • open-source ecosystems
  • competitive cloud AI offerings

Nice to have

  • Ph.D. or advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Data Science, Computer Engineering, or a related field
  • 15+ years of experience in technology leadership, applied science, AI/ML engineering, cloud infrastructure, distributed systems
  • Deep understanding of generative AI architectures
  • Strong knowledge of cloud infrastructure
  • Demonstrated ability to manage complex budgets, capacity plans, vendor dependencies, and executive-level trade-offs

What the JD emphasized

  • multimodal GenAI strategy
  • infrastructure, capacity, reliability, governance, and operating model
  • deliver those capabilities at scale
  • large scale
  • high-volume GenAI workloads
  • production-grade services
  • mission-critical GenAI platforms
  • end-to-end GenAI solutions
  • responsible AI practices
  • scalable platform capabilities
  • high-scale workloads
  • 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.

Other signals

  • multimodal GenAI strategy
  • infrastructure, capacity, reliability, governance, and operating model
  • deliver those capabilities at scale
  • large scale
  • high-volume GenAI workloads
  • production-grade services
  • mission-critical GenAI platforms
  • end-to-end GenAI solutions
  • responsible AI practices
  • scalable platform capabilities
  • high-scale workloads