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About This Role
About Zuora
Zuora is entering a new chapter in its evolution, with an opportunity to rethink how an established enterprise software company builds, delivers, and operates technology in an AI-native world.
As AI and agentic technologies reshape enterprise software, we believe the opportunity extends well beyond adding AI capabilities to existing products. It requires rethinking the underlying platforms, architecture, data foundations, and technical capabilities that enable intelligent systems to understand context, reason, take action, and deliver business outcomes. Zuora is investing in this transformation with the ambition to build a more intelligent, connected, and adaptable technology platform—one that accelerates product innovation, creates new customer value, and enables the company to operate with greater speed and efficiency. We are looking for a Vice President of AI Platform to help define and build that foundation. This leader will bring together deep technical expertise, platform strategy, and an entrepreneurial mindset to establish the capabilities that make AI-native product experiences possible across Zuora.
The Opportunity
The VP of AI Platform will define and lead the strategy, architecture, and development of the shared AI platform capabilities that underpin Zuora's next generation of products and experiences.
This is not simply a role focused on integrating large language models or deploying AI infrastructure. It is an opportunity to determine what the company needs to build, what it should leverage from the rapidly evolving AI ecosystem, and how to create a scalable platform that enables product teams to innovate quickly without fragmenting the architecture or duplicating foundational capabilities.
The successful candidate will work across Product, Engineering, Data, Security, and other technology leaders to establish the platform required to support AI-powered workflows, intelligent automation, and agentic experiences. They will translate an ambitious AI-native vision into a practical technical strategy, making thoughtful choices about architecture, investment, build-versus-buy decisions, and the capabilities that will differentiate Zuora. The mandate is both strategic and hands-on: establish the direction, build the foundational capabilities, and help the organization move from experimentation to production at scale.
Role & Responsibilities
Define the AI Platform Strategy and Architecture
- Develop the vision, strategy, and technical architecture for Zuora's AI platform, aligned with the company's broader AI-native transformation.
- Determine the foundational capabilities required to support AI-powered products, intelligent workflows, and agentic applications across the portfolio.
- Establish a coherent platform architecture that enables reuse, interoperability, extensibility, and rapid innovation across product teams.
- Make pragmatic build-versus-buy decisions, leveraging external models, services, and infrastructure where appropriate while investing in capabilities that create strategic differentiation.
- Establish a roadmap that balances near-term product opportunities with the long-term platform capabilities required to support the company's evolution.
Build the Foundations for AI-Native Products
- Develop shared capabilities that enable product teams to incorporate AI into customer experiences and business workflows without rebuilding foundational infrastructure for each use case.
- Establish the technical foundations for AI agents that can understand user intent, access relevant context, reason through tasks, interact with enterprise systems, and take appropriate action.
- Define approaches to model integration and orchestration, context management, retrieval-augmented generation, tool use, and agent execution.
- Enable secure and reliable access to enterprise data, business logic, APIs, and transactional capabilities.
- Develop reusable patterns and services that accelerate experimentation and reduce the time required to move AI capabilities from prototype to production.
- Ensure the platform supports both conventional AI-enabled experiences and more autonomous, multi-step agentic workflows as the technology matures.
Establish Enterprise-Grade AI Infrastructure and Governance
- Build a secure, reliable, and scalable platform that meets the requirements of enterprise customers and mission-critical business workflows.
- Establish standards for model evaluation, testing, observability, performance, and continuous improvement.
- Develop approaches to managing AI quality, latency, availability, and cost, including appropriate model selection and routing.
- Partner with Security, Legal, Privacy, and Compliance teams to establish appropriate controls for data access, model usage, agent permissions, and execution.
- Ensure that AI systems operate within defined authorization boundaries, with appropriate safeguards, traceability, and human oversight for consequential actions.
- Establish mechanisms to monitor production performance, identify failures, evaluate outcomes, and improve system behavior over time.
Drive Technical and Business Outcomes
- Translate platform investments into measurable business and customer outcomes, including faster product development, improved customer experiences, increased automation, and more efficient operations.
- Establish clear measures of platform adoption, developer productivity, reliability, AI quality, cost efficiency, and time to production.
- Prioritize platform investments based on business impact and the capabilities they unlock for multiple product teams.
- Ensure the platform evolves in response to advances in AI technology, emerging customer needs, and lessons from production deployments.
- Communicate architectural decisions, investment priorities, trade-offs, and progress clearly to executive leadership.
What Success Looks Like
The VP of AI Platform will be successful when Zuora has established a coherent, secure, and scalable AI foundation that materially accelerates the company's transition to AI-native products and operating models. Success will be reflected in:
- Faster innovation: Product teams can develop, test, and deploy AI-powered capabilities without repeatedly building foundational infrastructure.
- Production-grade AI: AI applications operate with appropriate levels of reliability, quality, security, observability, and cost control.
- Agentic capabilities: Zuora can build experiences that move beyond answering questions to executing meaningful business workflows within defined permissions.
- Platform adoption: Shared AI capabilities are used across product teams, reducing duplication and accelerating delivery.
- Customer and business value: Platform investments translate into more useful products, greater automation, and measurable improvements in customer and employee outcomes.
- A scalable technical foundation: The architecture can evolve as models, agent frameworks, and AI infrastructure continue to change.
- Organizational alignment: Product, Engineering, Data, and Security teams operate with a shared understanding of the platform strategy and how to build on it.
About You
We are looking for a technology leader who can build the AI platform that makes Zuora's transformation real, turning rapidly evolving AI capabilities into secure, reusable, enterprise-grade foundations that enable our products to understand intent, execute workflows, and deliver business outcomes.
Deep Technical and Architectural Expertise
- A proven technology leader with significant experience building and scaling platforms, infrastructure, or distributed systems in complex enterprise software environments. Strong understanding of modern AI architectures and the infrastructure required to support production-grade AI applications.
- Familiarity with large language models, model serving and orchestration, retrieval-augmented generation, embeddings, vector search, context engineering, and agentic application architectures.
- Experience designing systems that integrate AI capabilities with enterprise data, APIs, business logic, and transactional systems.
- Strong grounding in cloud architecture, distributed systems, security, reliability, and scalable software engineering.
- Ability to distinguish durable architectural principles from capabilities that are changing rapidly as AI technology evolves.
An AI-Native Platform Mindset
- Understands that AI-native software requires more than adding a model or conversational interface to an existing application.
- Thinks in terms of reusable capabilities, intelligent workflows, agents, orchestration, and systems that can act on behalf of users within defined boundaries.
- Can identify which capabilities should be foundational platform services and which should remain specific to individual products or use cases.
- Understands the importance of context, permissions, data access, evaluation, observability, and reliability when moving AI from demonstrations into real enterprise workflows. Maintains a practical perspective on the limitations of current AI systems and knows how to build architectures that accommodate rapid technological change.
A Builder Who Can Turn Strategy into Reality
- Has taken complex technical initiatives from initial concept through architecture, development, production deployment, and scaled adoption.
- Combines strategic thinking with the ability to engage deeply in technical decisions and challenge assumptions.
- Makes clear trade-offs between speed, flexibility, cost, reliability, and long-term architectural integrity.
- Knows when to build, when to buy, when to partner, and when to wait for the technology to mature.
- Is comfortable operating in an environment where the destination is clear but the technical and organizational path must be discovered and refined.
Strong Product and Business Orientation
- Understands that the purpose of a platform is not to build technology for its own sake, but to enable better products and business outcomes.
- Can connect platform architecture and investment decisions to customer value, product differentiation, developer productivity, and commercial impact.
- Works effectively with senior Product and Engineering leaders to identify the highest-value opportunities for AI.
- Prioritizes simplicity, reuse, and adoption rather than building unnecessarily sophisticated infrastructure.
- Uses measurable outcomes to evaluate whether platform investments are delivering value.
Enterprise Judgment and Leadership
- Has experience building technology that must meet demanding requirements for security, privacy, reliability, and enterprise-scale operations.
- Can establish standards and governance without creating unnecessary bureaucracy or slowing innovation.
- Communicates complex technical concepts clearly to technical teams, product leaders, and executive stakeholders.
- Influences across organizational boundaries and builds alignment around architecture, priorities, and shared ownership.
- Attracts strong technical talent and creates an environment where high-performing engineers can do their best work.
Comfortable Defining What Comes Next
- Is intellectually curious, adaptable, and energized by the pace of change in AI. Does not assume that established software architecture or organizational models will remain sufficient as AI capabilities evolve.
- Can separate genuine technical opportunities from hype and make sound decisions amid uncertainty.
- Brings the judgment to establish a clear direction without pretending that every answer is already known.
- Is motivated by the opportunity to help define the next generation of enterprise software platforms rather than simply operate an established one.
About the Team
Enable Product and Engineering Transformation
- Partner closely with Product and Engineering leadership to identify where shared AI platform capabilities can unlock meaningful customer value and accelerate product innovation. Help product teams rethink what becomes possible when AI can interpret intent and execute workflows rather than simply respond to user commands.
- Establish clear platform interfaces, development patterns, documentation, and tools that make AI capabilities accessible to product engineers.
- Reduce duplicated infrastructure, fragmented implementations, and unnecessary complexity across teams.
- Help define the operating model for how platform engineering, AI research and application development, product management, and data teams work together.
- Foster a culture of rapid experimentation, disciplined engineering, and learning from production outcomes.
Build and Lead a High-Impact Organization
- Build a high-caliber AI platform organization that combines technical depth, strong engineering judgment, and an outcome-oriented mindset.
- Attract and retain talent across AI infrastructure, platform engineering, distributed systems, and applied AI, as appropriate to the evolving mandate.
- Establish a culture of ownership, technical excellence, accountability, and pragmatic execution. Create an environment in which teams can move quickly while maintaining the reliability, security, and discipline required for enterprise software.
- Develop strong partnerships across the company, influencing through technical credibility and a clear articulation of platform value.
Benefits
Zuora offers a comprehensive total rewards package designed to support ZEOs' wellbeing, growth, and flexibility. While specific offerings may vary by country, we typically provide:
- Competitive compensation, variable bonus and performance-based reward opportunities, and retirement programs
- Medical, dental, and vision insurance
- Generous, flexible time off, plus paid holidays, wellness days, and a company-wide year-end break
- Paid parental leave (including fully paid leave for eligible ZEOs, subject to local policy)
- Learning & development stipend to support ongoing growth
- Opportunities to volunteer and give back, including charitable donation matching where available
- Mental wellbeing resources and support
- Benefits may vary by location; details will be shared during the interview process
#ZEOLife at Zuora
ZEOs (our employees) are empowered to take ownership, challenge the status quo, and make a real impact. We:
- Collaborate deeply across teams and regions
- Learn constantly and iterate often
- Build an inclusive, high-performance culture where people feel inspired, connected, and valued
Our Commitment to an Inclusive Workplace
Think, be and do you.
At Zuora, different perspectives, experiences, and contributions matter — everyone counts.
Zuora is proud to be an Equal Opportunity Employer committed to creating an inclusive environment for all. We do not discriminate on the basis of, and consider individuals seeking employment with Zuora without regard to, race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.
We encourage candidates from all backgrounds to apply. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us by sending an email to [email protected] (or local equivalent, where applicable).
Salary Context
This $271K-$406K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Zuora, 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 $212,160 based on 13,809 positions with disclosed compensation. This role's midpoint ($339K) sits 60% above the category median. Disclosed range: $271K to $406K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Zuora AI Hiring
Zuora has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Foster City, CA, US. Compensation range: $406K - $406K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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