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About This Role
Job Description
Why This Role? Why Now?
Customer experience is transforming through AI-driven innovation. At Zendesk, we don’t just embrace this change — we lead it. Our Professional Services team is revolutionizing how we help our customers implement and adopt AI by building an AI Delivery team and methodology. As the technical and product expert for our AI-powered Resolution Platform, the AI Services Consultant role is integral to delivering quick time to value while guiding our customers through their configuration and optimization.
Mission
As an AI Services Consultant at Zendesk, your mission is to provide technical solutions and remove roadblocks for your customers to drive resolutions on our AI Platform. You will act as a technical expert and trusted advisor, guiding customers through complex configurations with agility and precision. Your strong consulting skills and project management expertise will ensure seamless integration and optimization of AI solutions, driving transformative business outcomes.
Overarching Objective For The Role:
- 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
- Be the customer’s AI technical expert. Turn goals into the right configuration, integrations, and design choices, and remove blockers fast.
Lead smooth, on-time delivery. Run projects end-to-end with clear scope, timelines, and stakeholder alignment to deliver reliably. *
How You’ll Make an Impact
Strategic Accountabilities
- Technical Expertise & Guidance: Provide expert advice on the implementation and optimization of Zendesk AI products, ensuring customers leverage the full capabilities of the technology to meet their business goals.
- Change Management & Training: Facilitate change management processes and deliver consultative sessions to ensure smooth adoption and integration of AI solutions within customer organizations.
- Successful Project Delivery: Accountable for the timely and successful delivery of AI projects, meeting or exceeding customer expectations and maintaining high standards of quality.
- Customer Satisfaction: Maintain high levels of customer satisfaction, contributing to customer retention and potential upsell opportunities.
- Cross-Functional Collaboration: Work closely with internal teams, including Customer Success and Sales, to ensure alignment on the customer’s AI Roadmap and address any technical challenges that would block the customer from adopting AI.
- Tactical solutions for business outcomes: Ensure that AI solutions are effectively aligned with customer business goals, resulting in measurable improvements in customer experience and AI Adoption.
What You’ll Need to Succeed
Strategic Skills & Expertise
- Consulting Expertise: Strong consulting skills with the ability to assess client needs, develop strategic solutions, and provide expert guidance throughout the project lifecycle.
- Strong Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- Project Management: Oversee the planning, execution, and delivery of AI projects, ensuring they are completed on time, within scope, and meet quality standards.
- Technical Proficiency in AI: Deep understanding of AI technologies, particularly Zendesk AI products, with the ability to implement and optimize these solutions effectively.
- Agility and Adaptability: Ability to work effectively in diverse and dynamic environments, adapting to changing client needs and industry trends.
- Problem-Solving Abilities: Strong analytical and problem-solving skills, with the ability to identify challenges and develop innovative solutions.
- Continuous Learning: Commitment to staying updated on the latest AI trends and advancements, ensuring the delivery of cutting-edge solutions to clients.
Qualifications
- Must have a minimum of 3+ years of related experience in Consulting / Professional Services
- 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
- Fluent in English
- Deep understanding of REST apis and working with large JSON bodies
- Python experience is preferred
- Demonstrated experience in using adoption and health analytics to build predictive models and forecasts for churn and expansion; surface early risk signals and recommended mitigations to secure retention and growth
- Demonstrated experience designing and executing success plans or roadmaps that drive measurable customer outcomes
- 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.
- 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
The US annualized base salary range for this position is $106,000.00-$160,000.00. 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 $106K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($133K) sits 37% below the category median. Disclosed range: $106K to $160K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Zendesk AI Hiring
Zendesk has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, US, Austin, TX, New York, United States. Compensation range: $160K - $277K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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