Vp, Engineering Advanced Applications and Solutions

Oracle Oracle · Enterprise · United States

VP of Engineering role focused on productizing advanced research, including AI and robotics, into scalable enterprise products and platforms. This involves leading cross-functional teams, defining technical strategy, overseeing engineering delivery for OCI-based capabilities, and ensuring compliance-sensitive initiatives are ready for commercialization.

What you'd actually do

  1. Lead product management and engineering teams responsible for building and productizing cutting edge research into advanced AI and robotics driven products.
  2. Own product vision, technical strategy, engineering roadmap, delivery milestones, staffing plans, and release execution for assigned initiatives.
  3. Translate scientific, clinical, AI, data, and infrastructure requirements into scalable product architectures and engineering plans.
  4. Build high-performing teams across product management, software engineering, cloud architecture, data engineering, AI/ML engineering, platform engineering, UX, security, and program management.
  5. Establish engineering operating rhythms, including roadmap planning, sprint or milestone execution, design reviews, architecture reviews, launch readiness, and post-launch support models.

Skills

Required

  • Product management
  • Engineering leadership
  • Technical strategy
  • Roadmap definition
  • Engineering delivery
  • Cross-functional team leadership
  • AI/ML engineering
  • Cloud architecture
  • Data engineering
  • Robotics
  • Scalability
  • Reliability
  • Observability
  • Security
  • Data governance
  • Compliance

Nice to have

  • Healthcare
  • Life sciences
  • Agriculture
  • Genomics
  • Autonomous laboratories
  • Scientific computing

What the JD emphasized

  • productizing cutting edge research
  • AI infrastructure
  • regulated AI-enabled workflows
  • compliance-sensitive initiatives
  • enterprise-grade standards
  • AI model training
  • regulated healthcare or life sciences workloads

Other signals

  • productizing research
  • AI infrastructure
  • regulated AI-enabled workflows
  • AI and robotics driven products
  • enterprise-grade standards
  • OCI-based capabilities
  • AI model training
  • biomolecular foundation models
  • AI-driven diagnostics