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About This Role
Job Description
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Why This Role? Why Now?
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Customer experience is transforming through AI\-driven innovation. At Zendesk, we don’t just embrace this change — we lead it. As visionary architects of our AI\-powered Resolution Platform, AI Success Strategists shape and deliver long\-term AI success for our customers. This role transcends technical deployment; it is about owning the strategic AI roadmap, maximizing business impact, and builds executive partnerships from day one so AI initiatives start strong and scale predictably.
Mission
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You will own end\-to\-end delivery of customers’ AI roadmaps: translating product capabilities into clear business strategies, defining a long\-term AI vision, aligning internal and external teams, and driving execution from initial adoption through sustained expansion. Your success is measured by high customer satisfaction, increased automated resolution usage, and improved retention—while orchestrating the right mix of product, services, and partner resources. You will lead structured discovery and success\-planning engagements that create measurable playbooks and milestones early in the lifecycle.
Overarching Objective For The Role:
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- Accelerate customers’ time‑to‑value by driving adoption and operational excellence for Zendesk AI solutions, ensuring customers realize measurable business impact from initial deployment through scale
- Own customers’ multi‑year AI roadmap and execute cross‑functional delivery—aligning Product, Services, Sales, and Partners to scale safe, sustainable AI programs
- Deliver quantifiable commercial outcomes —protect renewals, grow account expansion, and translate AI adoption into predictable revenue and retention improvements
### How You’ll Make an Impact
Strategic Responsibilities
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- Full Ownership of AI Roadmap Delivery: Lead the design, planning, and execution of comprehensive AI adoption and expansion roadmaps for key customers, ensuring alignment to their broader business goals and customer success vision.
- Design Authority: Serve as the design authority for customers’ AI deployments — lead solution design reviews, ensure deployments align with the latest best practices and collaborate with Professional Services on integration and configuration guidance. For complex agent implementations, coordinate troubleshooting and technical escalations with Professional Services, Product and Engineering to resolve blockers quickly.
- Long\-term AI Vision \& Business Strategy: Partner with executive stakeholders to co\-create and refine a forward\-looking AI strategy that anticipates market trends and customer needs. Serve as the primary AI thought leader and trusted advisor throughout the engagement lifecycle.
- Internal and External Stakeholder Alignment: Dedicate effort to internal cross\-functional collaboration, aligning Product, Professional Services, Sales, Success, and other teams to deliver on the AI roadmap.
- Serve as the Engagement Lead: Coordinate and sequence activities, identify and escalate risks early, and ensure the right technical, product, and services resources are engaged at the appropriate times.
- Sustain Engagement \& Mutual AI Roadmap: Maintain recurring outcome\-driven cadences and mutual AI Roadmap that define strategic goals, shared initiatives, measurable success, and a roadmap to maximize value and secure renewal.
- Channel \& Adoption Strategy: Own the channel strategy for AI solution dissemination and adoption, balancing direct customer delivery with channel enablement and partner engagement.
- Measure and Drive Impact: Utilize data\-driven insights to track AI adoption, resolution metrics, and gross revenue retention improvements. Continuously refine strategies to optimize outcomes and customer satisfaction.
- Responsible AI \& Compliance: Confidently advise customers on ethical AI practices, data governance, and compliance considerations — framing use cases within safe, explainable, and policy‑aligned guardrails.
What You’ll Need to Succeed
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Strategic Skills \& Expertise
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- Project \& Program Management: Proven ability to orchestrate multi\-phase technology adoption projects, managing diverse teams and timelines with clarity and agility.
- Product and Technical Knowledge: Product expert who assesses customers' use case automation potential and technical readiness (generative AI, integrations, authentication), helps craft user journeys and translates features into value. Liaise with ProdDev teams to remove blockers for adoption, implement fixes and provide feedback on (beta) features.
- Business \& Strategic Acumen: Demonstrated skill in aligning AI solutions to customer business challenges, driving revenue growth, and influencing executive decision\-making.
- Consultation \& Communication: Strong stakeholder engagement skills—whether customer executives or internal teams—to advance AI adoption and sustain strategic partnerships.
- Analytical \& Outcome\-Focused: Strong analytical capacity to interpret adoption data, develop predictive health scores, forecast retention/expansion, and translate metrics into prioritized action plans.
- Forward\-Thinking \& Pragmatic: Visionary in embracing AI’s potential with a grounded, realistic approach to execution—balancing innovation with practical delivery feasibility.
Qualifications
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- Must have a minimum of 8\+ years of related experience in Customer Success / Experience, 2\+ years of AI related experience
- Previous experience in go\-to\-customer/GTM roles in enterprise technology / SaaS — either customer success management, professional services/consulting, technical account management, or solutions engineering/pre\-sales consulting
- Demonstrated experience in using adoption and health analytics to forecast churn and expansion; surface early risk signals and recommended mitigations to secure retention and growth.
- Bachelor’s degree in Business, Computer Science, Engineering, or related field; advanced degrees; certifications in AI strategy or project management preferred.
- Experience managing AI or software adoption programs with demonstrated impact on business metrics such as retention or satisfaction.
- Demonstrated experience designing and executing success plans or roadmaps that drive measurable customer outcomes
- Excellent program management and cross\-functional influence skills.
- Familiarity with emerging AI trends is a plus
- Ability to distill complex AI concepts for diverse audiences, especially executive stakeholders.
Great to have
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- AI Domain Expertise: Deep understanding of AI capabilities within Zendesk’s platform, not necessarily hands\-on technical, but sufficient to translate product features into business value and strategic initiatives.
The US annualized OTE (On Target Earnings) range for this position is $118,000\.00\-$178,000\.00 with a pay mix of 70/30 (base/commission). 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 $118K-$178K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($148K) sits 32% below the category median. Disclosed range: $118K to $178K.
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.
Zendesk AI Hiring
Zendesk has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $178K - $178K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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