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About the Role
We’re looking for an AI-native video and creative leader who can transform complex Oracle AI Database stories into compelling video experiences that capture attention and make technology feel accessible, relevant, and memorable.
This role sits at the intersection of storytelling, video, design, and generative AI. You’ll help define how Oracle AI Database shows up through video using emerging AI tools alongside traditional creative and production capabilities to develop high-quality content faster, experiment with new formats, and build a distinctive visual storytelling approach.
This isn’t a traditional video production role. We’re looking for someone who is as comfortable developing the creative concept as they are bringing it to life from idea and storyboard through AI generation, editing, animation, and final production.
What You’ll Do
- Lead video strategy and creative execution for Oracle AI Database across digital campaigns, web, social, events, executive storytelling, and product marketing.
- Turn complex AI, data, and database concepts into simple, compelling video narratives designed for technical and executive audiences.
- Concept, storyboard, produce, and edit high-impact video content using a combination of generative AI and traditional video production tools.
- Experiment with emerging AI video, image, voice, animation, and editing technologies to continually evolve how we create content.
- Develop repeatable AI-powered video workflows, templates, and creative systems that enable the team to produce high-quality content at greater speed and scale.
- Create a range of video formats, from short-form social and campaign creative to product stories, explainers, thought leadership, executive content and event experiences.
- Partner closely with product marketers, designers, content strategists, campaign leads, and executives to translate business priorities into breakthrough creative concepts.
- Maintain a high bar for visual craft, storytelling, brand consistency, and production quality across every touchpoint.
- Help establish best practices for responsible and effective use of generative AI in video creation.
Stay ahead of emerging AI creative technologies and identify opportunities to bring new tools, techniques, and formats into our marketing engine. *
Experience Requirements
- 3-6 years of experience in video, motion design, creative direction, content production, or related creative roles.
- Strong portfolio demonstrating exceptional video storytelling, creative concepting, motion, and visual craft.
- Hands-on experience with generative AI tools for video, imagery, animation, voice, or creative production.
- Deep proficiency in video editing and motion tools such as Adobe Premiere Pro, After Effects, or equivalent platforms.
- Strong understanding of video, pacing, composition, sound, motion, and visual hierarchy.
- Experience creating content for technology, B2B, enterprise, or other complex subject matter preferred.
- Demonstrated ability to take an idea from concept through final execution with a high degree of independence.
- Experience building repeatable creative workflows, templates, or production systems that can scale across teams and channels.
Strong collaborator who can work effectively with marketers, designers, executives, and technical subject matter experts. *
You’ll Thrive Here If You
- See generative AI as a new creative medium, not simply another production tool.
- Can take a complicated technical idea and immediately start imagining how to tell it visually.
- Are equal parts creative thinker and hands-on maker.
- Love experimenting with new tools and figuring out what’s possible before there’s an established playbook.
- Have exceptional taste and know the difference between content that is simply “AI-generated” and content that actually feels premium.
- Move quickly, iterate often, and can take an idea from a rough concept to polished execution.
- Want to help define what AI-native video storytelling looks like for one of the world’s leading enterprise technology brands.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Oracle AI Hiring
Oracle has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect, Research Scientist. Positions span Seattle, WA, US, Redwood City, CA, US, Seattle, WA.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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