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The Director, Global Strategic Pursuits Events \& Experiences will own the strategy, design, governance, and end\-to\-end execution of a global portfolio of strategic customer events and hospitality programs. The role will establish a dedicated operating model, manage multiple concurrent events, and serve as the single\-threaded leader across field marketing, sports marketing, sales, partner teams, regional stakeholders, agencies, vendors, and executives.
This leader will translate pursuit and account priorities into distinctive customer journeys; ensure content, speakers, venues, hospitality, transportation, and executive touchpoints are cohesive; and deliver each program on time, on budget, and in compliance with Oracle requirements. The role also will build scalable processes, develop in\-region execution capacity, and create a clear measurement framework for customer and business impact.
The role will also own alignment of BDR/SDR teams with field sales, strategic partners, and product organizations to ensure event investments translate into coordinated account engagement, pipeline acceleration, and measurable sales outcomes. Additionally, they will also lead field sales enablement for partner offerings, equipping sales teams with the knowledge, messaging, tools, and coordinated support from partner and product organizations needed to position joint solutions effectively, engage target accounts, and accelerate pipeline.
These programs are not standard field marketing events. They are high\-touch, customer\-specific experiences tied to strategic pursuits, executive engagement, partner alignment, and premium hospitality. The portfolio spans North America, EMEA, and APAC and requires a single accountable leader who can combine global consistency with strong in\-region execution, particularly in APAC.
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Key Responsibilities
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1\. Portfolio strategy and prioritization. Own the integrated global roadmap for near\-term and long\-range strategic events, including executive summits, industry conferences, ancillary programs, and sports hospitality. Prioritize investments based on pursuit importance, customer value, timing, feasibility, and available resources.
2\. Experience and customer\-journey design. Develop the event strategy and full customer journey for each program, including audience, objectives, agenda, venue, hospitality, activities, transportation, executive moments, and local\-market details that make the experience relevant and memorable.
3\. End\-to\-end program operations. Lead briefs, workback plans, budgets, registration, agencies, venues, vendors, food and beverage, transportation, guest communications, production, staffing, risk management, and on\-site delivery across multiple concurrent programs.
4\. Executive, content, and speaker alignment. Partner with pursuit leaders, account teams, subject\-matter experts, and executive offices to identify the right speakers and executive participants early; shape cohesive messaging; and ensure speakers are prepared for the customer and context.
5\. Global governance and regional execution. Create repeatable standards, templates, decision rights, and escalation paths while enabling local adaptation. Build strong in\-region support, especially in APAC, for venue sourcing, vendors, transportation, cultural considerations, contracting, compliance, and purchase orders.
6\. Cross\-functional leadership. Serve as the primary point of coordination across field marketing, sports marketing, sales, partner marketing, regional marketing, agencies, and executive stakeholders. Clarify ownership, maintain visible event progress in event management system (DISCO), and keep dependencies and decisions moving.
7\. Financial, compliance, and procurement management. Own program forecasts and budget controls; support executive and CEO\-office approvals where required; manage contracts, purchase orders, compliance submissions, and partner co\-investment or MDF processes; and surface risks early.
8\. Agency and vendor leadership. Select, brief, and manage agencies and specialized vendors; set service standards and deliverables; negotiate scope and value; and hold partners accountable for quality, timing, cost, and brand experience.
9\. Measurement and optimization. Define success measures for attendance, customer engagement, executive participation, account progression, experience quality, and operational performance. Conduct post\-event reviews and use insights to improve future programs.
10\. Team and capability building. Design the strategic events operating model, identify resource gaps, and lead or build a small dedicated team over time. Coach matrixed contributors and create a high\-accountability, calm, customer\-first execution culture.
11\. Lead field sales enablement for partner offerings by developing clear value propositions, customer use cases, sales plays, talk tracks, and account\-specific engagement strategies that help sellers position joint solutions effectively.
12\. Drive the full pre\-event sales activation motion, including target\-account selection, BDR/SDR outreach, customer registration, executive and seller meeting recruitment, agenda development, and coordination of partner and product participation.
13\. Align field sales, BDR/SDR, partner, product, and marketing teams on event goals, priority opportunities, meeting preparation, and post\-event follow\-up, measuring impact through attendance, meetings held, seller engagement, pipeline creation, and opportunity acceleration.
Required Qualifications
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- 10\+ years of progressive experience in global events, experiential marketing, executive programs, strategic account marketing, or a related discipline.
- Demonstrated ownership of complex, high\-touch, multi\-stakeholder programs across regions, including large six and seven\-figure event budgets.
- Strong program\-management discipline, with the ability to manage multiple concurrent workstreams, critical paths, approvals, and dependencies without losing strategic context.
- Experience leading agencies, vendors, and matrixed teams, with clear standards for quality, accountability, and financial stewardship.
- Executive presence and exceptional written and verbal communication skills; able to influence senior leaders, resolve ambiguity, and make sound decisions under time pressure.
- Working knowledge of contracting, procurement, compliance, purchase orders, registration, hospitality, venue operations, and event risk management.
- Experience leading integrated field sales enablement and event activation programs for partner, alliance, or joint technology offerings.
- Demonstrated success driving customer registration, executive meetings, account engagement, and structured sales follow\-up for strategic events.
- Proven ability to translate complex partner and product capabilities into actionable sales plays while coordinating effectively across field sales, BDR/SDR, product, partner, and marketing teams.
- Ability to travel domestically and internationally and support global time zones during critical planning and execution periods.
- Bachelor's degree or equivalent practical experience.
Preferred Qualifications
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- Experience in enterprise technology, cloud, AI infrastructure, strategic partnerships, and complex B2B customer environments.
- Experience designing executive\-level customer experiences tied to strategic pursuits or account progression, rather than stand\-alone brand events.
- Direct experience executing in APAC and EMEA, with strong cultural fluency and an established network of regional agencies or vendors.
- Experience with premium sports, entertainment, or cultural hospitality and with sponsorship or ancillary\-event activation.
- People leadership experience and success building a new operating model, function, or center of excellence.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Oracle AI Hiring
Oracle has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Agent Developer, Research Scientist. Positions span US, Nashville, TN, US, Santa Clara, CA, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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