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Job Summary
The Associate Director, AI & Intelligence will sit within the Brand, Marketing, & Communications (BMC) team and is responsible for shaping, optimizing, and monitoring how artificial intelligence systems and major AI assistants describe IEEE, its values and goals. As an increasing portion of the public forms its first impression of IEEE through AI assistants, this role will drive initiatives that advance the organization's strategy across Generative Engine Optimization (AEO/GEO), partnering closely with internal stakeholders in IT, Legal and Compliance, and BMC leadership. This role will report directly to the Director of Marketing Technology and Intelligence and will manage two direct reports.
Key Responsibilities
- Generative Engine Optimization (AEO/GEO): Define, publish and govern the enterprise AEO/GEO strategy across enterprise IEEE and OU web properties to ensure live AI retrieval engines can easily extract and accurately cite IEEE's true scope.
- Structured Entity Record Governance: Lead ongoing efforts to correct and enrich structured reference records, for enterprise IEEE and OUs. Navigate conflict-of-interest guidelines by managing engagement through verifiable third-party sources and public discussion processes, ensuring Wikidata accurately reflects parent-child links between IEEE and its OUs.
- Technical Crawlability & Infrastructure Alignment: Partner directly with Enterprise Digital and IT teams to perform regular AI crawler audits, verify that AI crawlers are permitted at network and content-delivery layers, analyze server logs, and ensure core web pages are structured for optimal AI extraction.
- Third-Party Consensus & Ecosystem Framing: Collaborate with BMC leadership to embed IEEE’s preferred framing across independent, authoritative third-party sources (e.g., press, partner descriptions, and reference sites). Drive the broad public consensus required to influence future AI model training datasets over time.
- AI Intelligence, Monitoring & Performance Analytics: Oversee the development and execution of an enterprise AI monitoring capability using commercial tools or internal scripts. Establish shared scorecards against priority test questions, track mention rates, framing accuracy, and cited sources across major AI assistants, and deliver executive reporting and recommendations for senior leadership. Integrate AI representation metrics into BMC’s broader marketing intelligence and performance framework in partnership with the Marketing Intelligence & Analytics team.
- Operating Unit (OU) Strategic Enablement: Act as a central consultant and partner to individual OUs, assisting unit leaders in publishing localized plain-text definitions, validating unit-level entity records, and closing AI visibility gaps.
- AI Governance & Policy: Shape and maintain the policies and guidelines within which BMC staff and other marketing resources operate as AI intersects IEEE’s digital presence, including standards for AI generated content, disclosure, and brand representation, partnering with BMC’s digital governance function, IT, and Legal and Compliance, and aligning with the enterprise AI strategy.
- AI Enablement & Marketing Innovation: Identify, pilot, and scale high-value AI use cases across BMC’s marketing practice—from content operations to insight generation—building AI fluency across the team and ensuring adoption follows established governance and responsible-use guidelines.
- People & Roadmap Management: Lead cross-functional workstreams—with the potential for direct reports as the team evolves—managing ad-hoc AI and digital strategic projects while executing a phased, multi-quarter roadmap covering fast wins, durable ecosystem changes, and continuous measurement.
Travel Information
- 25% Domestic and International
Qualifications
Education
- Bachelor's degree or equivalent experience
- Bachelor’s degree required (degree in Communications, Computer Science, Data Science, Information Systems, or Marketing Technology preferred). Required
Work Experience
- 7–10+ years of progressive experience leading Search/Generative Engine Optimization (SEO/AEO/GEO), Digital Strategy, Marketing Technology, or Data Intelligence functions within a complex global organization preferred
Skills and Requirements
- AI Systems & AEO/GEO Expertise: Deep operational understanding of how AI systems retrieve live web content, interact with structured entity records, and process standard organization markup. Demonstrated track record driving digital transformation and AEO/GEO initiatives.
- Data Strategy & AI Analytics: Proven ability to build intelligence monitoring frameworks, track brand accuracy across AI platforms, and translate AI performance metrics into executive reporting and strategic recommendations.
- Governance & Cross-Functional Alignment: Demonstrated experience establishing and enforcing enterprise digital and AI policies, content standards, and structured markup guidelines across matrixed business units and technical teams.
- AI Enablement: Hands-on familiarity with generative AI platforms and a track record of introducing AI tools and workflows into marketing or communications teams, with sound judgment on responsible use.
- Leadership: 3–5+ years of direct experience leading cross-functional teams, direct reports, or technical initiatives.
Other Requirements:
As defined in IEEE Policies, individuals currently serving on an IEEE board or committee are not eligible to apply.
PLEASE NOTE: This position is not budgeted for employer-sponsored immigration support, this includes all persons in F (both CPT and OPT), J, H, L, or O status.
For information on work demands and conditions required for this position, please consult the reference document, ADA Requirements. This position is classified under .
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Disclaimer: This job description is proprietary to IEEE. It outlines the general nature and key features performed by various positions that share the same job classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties and qualifications required of all employees assigned to the job. Nothing in this job description restricts management’s right to assign or re-assign duties to this job at any time due to reasonable accommodations or other business reasons.
$127,000 $186,000Job
: Data & AnalyticsPrimary Location
: United States-New Jersey-PiscatawaySchedule
: Full-timeJob Type
: RegularJob Posting
: Sep 21, 2026, 7:37:28 AM
Salary Context
This $127K-$186K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At IEEE Corporate, 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 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $127K to $186K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
IEEE Corporate AI Hiring
IEEE Corporate has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Piscataway, NJ, US. Compensation range: $186K - $186K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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