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About This Role
Job Description
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As Global Director of Sales Operations, you will help lead the transformation of Zendesk’s GTM operating model by redesigning workflows, embedding AI and automation into core business processes, and improving operational effectiveness across the customer lifecycle.
This role goes beyond traditional Sales Operations. You will help design and implement new approaches to how work gets done, leveraging AI, automation, and modern GTM tooling to solve problems more effectively and drive measurable business outcomes. This includes designing and operationalizing intelligent AI agents and agent\-assisted workflows that augment GTM teams, automate execution, and improve decision\-making across the customer lifecycle.
We are looking for a hands\-on builder, someone who is comfortable operating at both the strategic and execution level, willing to get into the details, and able to iteratively design, test, and refine solutions. You will leverage AI, automation, and modern GTM tooling to solve problems more effectively and drive measurable business outcomes.
What You’ll Do
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- Design, build, and maintain AI agents and agentic GTM experiences that support sellers, managers, and customer\-facing teams through intelligent task execution, workflow orchestration, conversational guidance, and autonomous or semi\-autonomous operational workflows.
- Lead the evolution of an AI\-first GTM operating model, driving operational excellence, workflow redesign, and scalable automation across the customer lifecycle.
- Own and prioritize the GTM systems and automation roadmap, translating business needs into scalable solutions that connect data, workflows, AI, and automation to improve execution and productivity.
- Lead cross\-functional transformation initiatives focused on process improvement, AI adoption, workflow standardization, and scalable operational change across the GTM organization.
- Establish governance and operating standards across GTM systems, data, and workflows, including intake, approvals, change management, data stewardship, and responsible use of AI and automation.
- Enable efficient and scalable deal operations and GTM launches, embedding automation and intelligent guidance into approval, pricing, execution, and launch\-readiness workflows.
- Lead and develop a team of GTM operations specialists, fostering accountability, continuous improvement, and the design and iteration of AI\-powered solutions.
- Operate with a structured, iterative delivery model, managing prioritization, sprint\-based execution, cross\-functional alignment, and measurable business outcomes.
- Partner across Sales, Marketing, Customer Success, Data, and IT to drive adoption, align priorities, and lead cross\-functional transformation initiatives across the GTM organization.
What You Bring
- 7\+ years of experience in GTM Operations, Revenue Operations, Automation, Systems, Strategy, or related roles within high\-growth B2B environments, including experience leading operational or technical teams.
- Proven ability to lead large\-scale, cross\-functional transformation initiatives that improve operational effectiveness, execution quality, and business performance.
- Strong systems\-thinking and problem\-solving capabilities, with the ability to translate ambiguous business challenges into scalable operational and technical solutions.
- Experience designing and operationalizing AI\-enabled and agentic GTM workflows across areas such as sales engagement, lead routing, prioritization, sequencing, next\-best actions, guided selling, forecasting, AI\-assisted execution, conversational copilots, and autonomous workflow orchestration.
- Experience building or supporting AI\-powered productivity workflows, including automated sales collateral generation, business case creation, customer\-facing content, and rep enablement processes.
- Experience working with modern automation, orchestration, and AI tooling (e.g., Make, n8n, Zapier, Workato, Clay, Claude, Codex, Cortex, or similar platforms) to improve productivity and streamline execution.
- Familiarity with modern GTM tooling ecosystems, including CRM, sales engagement, conversational intelligence, forecasting, enrichment, and workflow automation platforms.
- Strong understanding of data governance, enrichment, and signal\-driven operating models, with experience improving data quality, operational reliability, and business decision\-making.
- Experience operating within iterative or agile delivery environments, managing prioritization, cross\-functional execution, and continuous improvement across complex initiatives.
- Ability to operate effectively across strategic and executional levels, partnering with executive, business, operational, and technical stakeholders to drive alignment, adoption, and measurable outcomes.
Who You Are
- You think in systems, understanding how data, workflows, tooling, automation, and AI connect to drive effective GTM execution.
- You are energized by building and improving how work gets done, not simply maintaining existing processes.
- You focus on measurable outcomes and business impact, holding yourself and your team accountable for execution and results.
- You are comfortable operating across levels, from executive strategy discussions to hands\-on problem\-solving with operational and technical teams.
- You operate with a bias toward action, iterating quickly, navigating ambiguity effectively, and continuously improving workflows and processes over time.
- You think beyond static workflows, designing intelligent and adaptive agentic experiences that combine AI, automation, data, and human guidance to improve GTM execution and scalability.
- You value operational rigor, including clean data, scalable processes, clear documentation, and reliable systems that support frontline teams effectively.
- You understand that successful transformation requires strong change management, ensuring new capabilities are not only launched, but adopted and used effectively.
LI\-LM5
The US annualized base salary range for this position is $174,000\.00\-$262,000\.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.
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 [email protected] with your specific accommodation request.
Salary Context
This $174K-$262K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $174K to $262K.
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.
Zendesk AI Hiring
Zendesk has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, US, CO, US. Compensation range: $241K - $262K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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