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Senior Principal Product Marketing Director, Events and Program Management Office, Oracle AI Database Product Marketing
Oracle is seeking a highly organized, strategic, and execution\-focused Events Manager to drive the planning, delivery, and operational excellence of Oracle AI Database Product Marketing events and programs. This role combines world\-class event management with Project Management Office (PMO) responsibilities, ensuring that Oracle AI Database events and strategic initiatives are delivered with precision and measurable business impact.
The ideal candidate thrives in fast\-paced environments, excels at cross\-functional coordination, and can seamlessly balance event execution with program governance, planning, and operational management. Approximately 80% of the role focuses on event strategy and execution, while 20% is dedicated to PMO activities supporting the Product Marketing organization.
Events Management (80%)
Strategic Event Planning
- Develop and execute a comprehensive events strategy supporting Oracle AI Database business objectives, customer engagement, demand generation, and thought leadership initiatives.
Manage a portfolio of events, including:
- Oracle AI World and Oracle AI World Tour
- Customer and executive briefings
- Industry conferences and trade shows
- Webinars and virtual events
- Partner with Product Marketing leaders to align event objectives, messaging, content, and audience engagement strategies.
Event Execution
- Lead end\-to\-end event planning, logistics, and delivery.
- Develop event timelines, budgets, project plans, and success metrics.
- Coordinate with internal stakeholders including Product Marketing, Product Management, Development, Sales, Corporate Communications, Corporate Marketing, Digital Marketing, Customer Success, and Executive Leadership.
- Manage external vendors, agencies, venues, production teams, and contractors.
- Oversee speaker management, executive preparation, agenda development, and content coordination.
- Ensure consistent branding, messaging, and customer experience across all event touchpoints.
Measurement and Optimization
- Define event KPIs and success metrics.
- Track event performance, attendance, engagement, pipeline influence, and customer impact.
- Deliver post\-event analysis and recommendations for continuous improvement.
- Maintain event dashboards and executive reporting.
Budget Management
- Manage event budgets and forecasting.
- Track expenditures and vendor contracts.
- Identify cost optimization opportunities while maintaining high\-quality event experiences.
PMO and Operational Excellence (20%)
Program Management
- Support Product Marketing leadership in managing strategic initiatives, planning cycles, and organizational priorities.
- Maintain project plans, milestones, dependencies, and action items for key AI Database marketing initiatives and projects.
- Drive accountability across cross\-functional teams to ensure on\-time delivery of commitments.
Governance and Reporting
- Develop and maintain program dashboards, status reports, and executive summaries.
- Facilitate weekly and monthly business reviews.
- Track project health, timelines, deliverables, and resource allocation.
Process Improvement
- Identify opportunities to streamline workflows and improve operational efficiency.
- Establish repeatable project management best practices across the Product Marketing organization.
- Support annual planning, quarterly business reviews, and organizational readiness initiatives.
Qualifications
Required Qualifications
- Bachelor's degree
- 5\+ years of experience in event management, marketing operations, program management, or related disciplines.
- Proven experience managing large\-scale corporate events and executive programs.
- Strong project management and organizational skills.
- Experience managing multiple concurrent projects and competing priorities.
- Excellent communication, stakeholder management, and executive presentation skills.
- Strong analytical and reporting capabilities.
- Experience working with cross\-functional global teams.
Preferred Qualifications
- Self\-starter with the ability to work with minimal supervision and strategically prioritize multiple tasks while coordinating activities across multiple marketing, development, field, and sales organizations. Ability to parallel process multiple projects with minimal supervision is key to this role.
- 8\+ years of B2B event management expertise, ideally at a leading cloud provider, within cloud database, data, AI, or ML domains (preferably for enterprise products).
- An understanding of cloud database technologies, agentic AI, generative AI, large language models, ML, and data management. Direct experience marketing cloud\-based database or AI\-driven data platform products at events is strongly preferred.
- Exceptional written and verbal communication skills, with the proven ability to independently develop high\-impact marketing assets—including customer presentations, product launch materials, and product or feature blogs—for both customer\-facing and executive audiences, often on short notice.
- Prior success in leading global event execution for enterprise cloud products, and marketing campaigns—including distilling complex ideas into compelling content, webinars, and events for a multitude of audiences across industries and geographies.
- The aptitude and desire to work on both strategic programs and tactical implementations.
- Experience within enterprise software, cloud computing, AI, database technologies, or B2B technology marketing.
- PMP certification or equivalent project management training.
- Experience supporting Product Marketing or Product Development organizations.
- Familiarity with event management platforms, marketing automation tools, CRM / CX systems, and project management software.
- Experience managing executive\-level events and customer engagement programs.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Oracle AI Hiring
Oracle has 22 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer, MLOps Engineer. Positions span US, Seattle, WA, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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