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About This Role
Job Description
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We are looking for a hands\-on CRM Platform Tech Lead to act as the technical anchor for our CRM engineering team. While our architects focus on the multi\-year, cross\-platform roadmap, your focus will be on the "here and now"—turning complex business requirements into elegant, scalable solutions and getting them across the finish line.
In this role, you will be the go\-to technical expert for a squad of developers and administrators. You won't be managing people in an HR capacity; instead, you will be in the trenches with the team, leading by example. You will own the technical delivery of sprint commitments, tackle the most complex coding challenges, and champion an AI\-first mindset across the squad to fundamentally change how we build and deliver technology. By actively mentoring the team, you will elevate their technical skills, engineering standards, and ability to leverage modern AI tools.
### What You Will Do
- Drive Solution Engineering: Take high\-level requirements and architectural guidelines and translate them into executable, step\-by\-step technical solutions (e.g., deciding when to use a Flow vs. Apex, designing complex triggers, or structuring custom UI components).
- Champion AI \& Productivity: Cultivate an AI\-first mindset within the engineering team. Leverage AI\-assisted coding and testing tools (e.g., GitHub Copilot, CRM\-native AI features) to accelerate sprint delivery, reduce technical debt, and automate repetitive administrative tasks.
- Lead Technical Delivery: Act as the technical heartbeat of the Agile squad. Drive sprint execution, ruthlessly unblock developers, and ensure the team meets its delivery commitments with high\-quality, bug\-free output.
- Hands\-on Development: Roll up your sleeves to build the most complex, business\-critical features yourself. You will lead the charge on heavy integrations, advanced custom coding, and intelligent, AI\-driven CRM solutions.
- Mentor \& Elevate the Team: Actively mentor junior and mid\-level engineers. Run pair\-programming sessions, enforce development best practices, and lead comprehensive code reviews to ensure long\-term system health.
- Own Code Quality \& Release Hygiene: Partner with QA and Release Management to ensure deployments are seamless. Enforce strict version control, CI/CD best practices, and automated testing standards.
- Collaborate Across GTM: Work daily with Product Managers, Business Analysts, and RevOps stakeholders to negotiate technical trade\-offs, scope level of effort, and ensure the technical reality matches the business expectation.
### What You Bring
- Experience: 10\+ years of hands\-on technical experience developing on enterprise CRM platforms (e.g., Salesforce), with at least 3\+ years acting as a technical lead, senior developer, or squad lead.
- Deep Technical Chops: Expert\-level proficiency in CRM development languages and frameworks (e.g., Apex, LWC, SOQL, REST/SOAP APIs, and complex declarative automation).
- AI\-First Mindset: A forward\-thinking approach to software engineering, with a strong interest in—or hands\-on experience using—AI tools to enhance developer productivity, streamline testing, and build smarter business systems.
- Delivery Focus: A strong bias for action and execution. You know how to balance "perfect" code with the need to deliver business value quickly and iteratively.
- Mentorship Mentality: A proven track record of upskilling peers. You enjoy teaching others and take pride in elevating the engineering standards of your entire team.
- Agile Proficiency: Deep understanding of Agile development lifecycles, sprint planning, backlog grooming, and CI/CD deployment models.
- Communication: Excellent ability to explain complex technical constraints or solutions to non\-technical stakeholders (Product Managers, Sales Ops, etc.).
### Bonus Points
- Advanced CRM Certifications (e.g., Salesforce Platform Developer II, Data Architecture, or Integration Architecture).
- Direct experience implementing AI solutions within a CRM (e.g., Salesforce Einstein, predictive forecasting, AI chatbots).
Familiarity with adjacent GTM tools in the Lead\-to\-Cash ecosystem (CPQ, Marketing Automation, Billing).
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Why Join Us?
- Lead the transformation of our GTM technology through innovative solutions, AI, and scalable execution.
- Work closely with business leaders and engineering teams to directly impact revenue growth and operational efficiency.
- Be part of a collaborative, forward\-thinking tech team with access to the latest enterprise tools and frameworks.
- Enjoy a competitive salary and a comprehensive benefits package designed to support you in and out of the office.
The US annualized base salary range for this position is $161,000\.00\-$241,000\.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 $161K-$241K range is above the median 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($201K) sits 6% below the category median. Disclosed range: $161K to $241K.
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
AI roles in Austin pay a median of $214,343 across 143 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 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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