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Focuses on clearly identifying user needs or areas of opportunity related to business and anticipating them to craft problem statements, user persona(s), and goal(s) that will meet or exceed the user expectations. Evaluates user experiences to assess discoverability, usability, accessibility, and desirability. Identifies and creates the right method (e.g., user journeys, wireframes, information architecture, user flow diagrams, and/or mockups) to design and communicate the desired user experience. Begins to lead cross\-functional teams to work on multiple design projects from concept through delivery, ensuring quality throughout the full cycle. Gathers and/or analyzes feedback from end users following release and identifies key findings.
Key Responsibilities
AI Experience:
- Lead the experience strategy for AI\-powered capabilities across Oracle Database products.
- Design intuitive, user\-centered workflows for AI\-assisted experiences, including copilots, conversational interfaces, intelligent recommendations, and guided workflows.
- Define user journeys, interaction models, information architecture, wireframes, and prototypes that simplify complex database tasks.
- Establish design principles and best practices for AI interactions that promote trust, transparency, discoverability, and explainability.
- Partner with Product Management to ensure customer experience considerations are incorporated into product strategy and roadmap planning.
Customer Advocacy:
- Champion the voice of the customer across cross\-functional product teams.
- Synthesize research findings into clear, actionable recommendations that influence product priorities, design decisions, and engineering implementation.
- Present research insights, usability findings, and experience recommendations to Product, Engineering, and executive leadership.
- Drive customer\-centered decision making across multiple Database initiatives.
Defining User Needs:
\-Identifies and leverages existing internal and external patterns for multiple projects, while providing guidance to junior staff to ensure consistency across projects.
\-Establishes collaboration with stakeholders, subject matter experts, fellow designers, product teams, and development to enable greater understanding of the relevant domain and the service blueprint for a given project.
\-Focuses on clearly identifying user needs or areas of opportunity related to business and anticipating them to craft problem statements, user persona(s), and goal(s) that will meet or exceed the user expectations.
\-Defines and leads establishment of directions regarding the full ecosystem of touch points, workflows, and other elements of the user experience journey.
Research Execution:
\-Analyzes user research data to capture user goals and arrive at the best approach to telling the story of user needs and of the designed solution to the problem statement.
\-Utilizes and/or conducts various research methods (e.g., qualitative, quantitative) with end users and other relevant stakeholders where needed, to understand purpose and ensure a viable solution is identified.
\-Identifies the need for user research and design\-led approaches within Oracle.
Designing and Prototyping\-Designing:
\-Identifies and creates the right method (e.g., user journeys, wireframes, information architecture, user flow diagrams, and/or mockups) to design and communicate the desired user experience.
\-Tests and refines the design based on quality, regulatory compliance, safety, and accessibility and catches improvement opportunities / gaps in the solution or approach before its finalization.
\-Develops detailed conceptual model definition, ensures design coherence and consistency with the conceptual model, and thoughtfully extends/adapts the model based on user feedback and usage data.
\-Leverages the existing design systems, style guides, patterns, templates, and components where possible and adapts where needed; begins to advise junior team members where needed.
\-Creates new patterns and experiences when existing patterns are not able to meet the identified user needs.
\-Actively participates in design reviews within the design team and leads peer review within their area of work.
\-Presents concepts, ideas, and feasibility to key stakeholders (e.g., product developers, management, end users, marketing) to establish common understanding of the designs, gain buy in, and ensure timelines can be met.
\-Presents and communicates design processes, recommendations, alternatives, and trade\-offs effectively with senior directors and across teams during design reviews.
\-Ensures team members have the necessary resources to execute on design goals.
Basic Qualifications:
- Bachelor's degree in Human\-Computer Interaction, Human Factors, Psychology, Cognitive Science, Design, Computer Science, Information Sciences, or a related discipline.
- 10\+ years of experience in User Experience Design, Product Design, UX Research, Human Factors, or related fields.
- Demonstrated experience designing enterprise software or cloud platform experiences.
- Strong portfolio showcasing experience design and user research across complex software products.
- Experience planning and conducting qualitative and quantitative user research.
- Expertise translating research findings into product and design recommendations.
- Experience designing AI\-assisted experiences, conversational interfaces, or intelligent product workflows.
- Strong understanding of user\-centered design methodologies, interaction design, usability principles, and information architecture.
- Experience using modern design and research tools such as Figma, Qualtrics, Maze, or similar platforms.
- Excellent written, verbal, and presentation skills with the ability to influence cross\-functional stakeholders.
Preferred Qualifications:
- Experience with database technologies, cloud infrastructure, developer tools, or enterprise software platforms.
- Familiarity with Generative AI, Large Language Models, intelligent assistants, and AI\-driven product experiences.
- Experience defining customer personas and customer journey frameworks.
- Knowledge of product analytics and experience measurement techniques.
- Experience working in agile software development environments.
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Oracle AI Hiring
Oracle has 15 open AI roles right now. They're hiring across AI Agent Developer, AI Engineering Manager, AI/ML Engineer, AI Software Engineer. Positions span US, Seattle, WA, US, Redwood City, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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