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About This Role
Job Description
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As a pivotal senior individual contributor, the Senior Marketing Operations Manager, Martech \& AI Transformation will architect a modern, intelligent marketing ecosystem. You aren't just managing tools; you are driving the shift from traditional automation to AI\-driven orchestration.
Reporting to the Director of Marketing Operations, you will be the cross\-functional lead responsible for Martech cohesion, AI\-readiness, and the adoption of high\-impact technologies. You will bridge the gap between "AI\-curious" and "AI\-native," piloting cutting\-edge tools (GenAI, agentic workflows, predictive modeling) while ensuring our core stack remains secure, integrated, and optimized for revenue.
What you’ll be doing
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Architect an AI\-Ready Ecosystem
- Define and evolve an organization\-level Martech architecture that prioritizes data fluidity and AI accessibility.
- Lead the Unified Data Layer initiative: ensure our CDP and Data Warehouse are structured to feed LLM\-based applications and predictive models accurately.
- Build and maintain a multi\-quarter roadmap that balances legacy stack debt with high\-growth AI innovation.
Drive AI Transformation \& Rapid Prototyping
- Own the AI Sandbox: Design and lead structured proofs\-of\-concept (POCs) for GenAI use cases, such as automated content localization, autonomous SDR agents, and hyper\-personalized journey mapping.
- Scale Intelligence: De\-risk major technology bets by running pilot programs and successfully transitioning them into full\-scale production workflows.
- Lead Vendor Innovation: Own the evaluation (RFPs, scoring, pilots) of emerging AI vendors, ensuring they meet enterprise standards for security and performance.
Modernize, Audit, and Rationalize the Stack
- Conduct regular audits to identify redundancy and capability gaps, producing consolidation recommendations.
- Streamline the stack by decommissioning underperforming tools to reallocate budget toward high\-impact AI and automation infrastructure.
AI Governance, Measurement, and Change Management
- Define AI Efficiency: Establish KPIs that measure not just conversion lift, but the reduction of manual work and increased speed\-to\-market.
- Codify Governance: Develop the framework for ethical AI use, data privacy, and brand consistency across all automated and generated outputs.
- Enable the Team: Act as the internal evangelist for AI adoption, creating runbooks, prompt\-engineering guides, and integration diagrams to upskill the broader marketing organization.
Strategic Partner \& Business Liaison
- Align marketing leaders on resources and dependencies; translate technical AI capabilities into business outcomes (e.g., MQL conversion, pipeline velocity).
- Facilitate working agreements with IT/CIO to ensure Martech\-led AI initiatives align with broader corporate data security policies.
What you bring to the role
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- AI\-First Systems Thinker: You look beyond simple integrations to architect cross\-functional solutions that leverage machine learning and agentic workflows to simplify decision\-making.
- Business‑oriented marketer: Solid understanding of the full marketing funnel and how technology, processes, and data drive conversion and pipeline. You can link platform capabilities to tangible business outcomes (lead quality, conversion lift, pipeline contribution).
- Technical Translator: The ability to explain complex concepts to non\-technical stakeholders in a way that gains buy\-in.
- Bias for Action: Proven experience driving large, multi\-quarter programs from a "wild idea" to a measured, scalable business reality.
- Data\-Obsessed: You don't just "want data"; you understand how to structure it for model training and predictive accuracy.
- Strong stakeholder influence: Proven ability to align senior leaders, run workshops, and codify governance that drives adoption.
Qualifications
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Must\-have
- 8\+ years of professional experience in Marketing Operations, Martech, or RevOps.
- Demonstrated AI Experience: Proven track record of operationalizing AI/ML tools \- moving beyond basic ChatGPT prompts to building automated, AI\-driven workflows within a production environment.
- Platform Expertise: Deep familiarity with the classic Martech stack (Salesforce, Marketo/HubSpot, Segment/Adobe RTCDP, GA4\) and how they interface with modern AI layers.
- Program Management: Experience leading stack rationalization, vendor selection, and large\-scale transformation initiatives.
- Governance Guru: Solid understanding of data privacy (GDPR/CCPA) and the specific security risks associated with feeding customer data into LLMs.
Nice\-to\-have
- Hands\-on experience with Integration/Middleware (Workato, Zapier, Tray.io) to build automated agentic workflows.
- Familiarity with Prompt Engineering, RAG architectures, or scripting (Python/SQL) for data manipulation.
- Experience in a high\-growth B2B SaaS environment.
- Advanced certifications in Salesforce, Marketo, or specialized AI/Data Science credentials.
\#LI\-WO1
The US annualized base salary range for this position is $158,000\.00\-$238,000\.00\. This position may also be eligible for bonus, benefits, or related incentives. While this range reflects the minimum and maximum value for new hire salaries for the position across all US locations, the offer for the successful candidate for this position will be based on job related capabilities, applicable experience, and other factors such as work location. Please note that the compensation details listed in US role postings reflect the base salary only (or OTE for commissions based roles), and do not include bonus, benefits, or related incentives.
Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration \- while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in\-office schedule is to be determined by the hiring manager.
The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, \& inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here .
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre\-employment testing, or otherwise participate in the employee selection process, please send an e\-mail to peopleandplaces@zendesk.com with your specific accommodation request.
Salary Context
This $158K-$238K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Zendesk, 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 Required
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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 19% above the category median. Disclosed range: $158K to $238K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Zendesk AI Hiring
Zendesk has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Austin, TX, US. Compensation range: $238K - $308K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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