Interested in this AI/ML Engineer role at Zuora?
Apply Now →About This Role
### About Zuora
At Zuora, we help businesses grow smarter and adapt faster. Our platform powers modern business models — from subscriptions and usage\-based pricing to AI\-driven and outcome\-based offerings — helping companies launch new products, automate complex billing, and unlock predictable, recurring revenue.
We've led the Subscription Economy for more than a decade. Now we're evolving again by building the definitive platform for quote to cash and helping companies monetize their products and services with an adaptable, AI\-ready foundation.
### The Opportunity
A technical SaaS sales engineer who captures the technical win on complex billing and quote\-to\-cash opportunities — with real fluency in consumption/usage\-based monetization and AI\-native environments. You'll scope, sell and build the QTC solution, not just demo it. Telecom experience is a strong plus.
This is a hands\-on Solutions Engineer role. Additionally, you'll also stay close to your accounts post\-sale — protecting technical health and surfacing expansion.
In this role, you will:
- Own the technical win. Partner with AEs through complex enterprise cycles — discovery, technical strategy, objection handling, and clear differentiation — to secure the technical decision.
- Scope, sell, and build quote\-to\-cash solutions. Translate messy customer requirements across CPQ, billing, and revenue into a defensible QTC architecture — including consumption and usage\-based billing models (metering, rating, mediation, invoice presentation, and downstream rev rec implications).
- Build and manage the lifecycle of proofs of concept as a primary mechanism (think forward deployed engineer)
- Go deep on consumption and AI\-native monetization. Design and demonstrate how customers monetize usage\- and AI\-driven products — event/usage capture, rating at scale, and the billing and revenue implications of consumption pricing.
- Build tailored, end\-to\-end demos and POCs aligned to each customer's strategic objectives — hands\-on with APIs, data, and integrations, not slideware.
- Lead technical presentations for senior stakeholders, including C\-level, translating architecture and pricing\-model tradeoffs into business value.
- Build strong technical relationships with customers and prospects — understanding their goals, constraints, systems of record, and operating models.
- Protect technical health and adoption on your accounts — ensure customers realize value against agreed outcomes, and identify expansion across business units, cross\-sell, and add\-ons.
- Influence roadmap by feeding structured customer and market signal to Product and Solutions teams.
- Coordinate technical resources across Zuora and partners; contribute to RFPs/RFIs, field events, and customer references; support escalations cross\-functionally.
- Travel within your territory as needed.
### About You
You are an experienced presales or solutions professional with a strong track record of owning technical wins in complex enterprise environments.
You likely bring:
- 7\+ years in Sales Engineering, Presales, or Solution Architecture — with demonstrated, hands\-on ownership of technical wins on complex enterprise deals.
- Direct billing / quote\-to\-cash experience — you've scoped, sold, and/or built solutions spanning CPQ, billing, and revenue (not just adjacent SaaS).
- Consumption / usage\-based billing fluency — metering, rating, mediation, and the revenue implications of consumption pricing
- Comfort in AI\-native environments — you understand how AI\-driven and usage\-based products get monetized, and can architect for that.
- Technical depth — APIs, integration patterns, data/reconciliation, and modern application architecture. You can debug a failing integration under time pressure, not just describe one.
- Telecom experience is a strong plus — high\-volume mediation, rating, and complex usage models.
- Excellent executive communication — you shift registers between engineers, controllers, and C\-level without losing precision.
- Structured sales methodology experience (e.g., MEDDIC, Value Selling).
- Bachelor's degree and/or relevant FinTech / enterprise software background.
A strong plus:
- Telecom experience, especially with high\-volume mediation, rating, and complex usage models
### About the Team
You will be part of a team focused on winning and expanding complex enterprise quote\-to\-cash opportunities by combining technical depth, business acumen, and customer partnership.
This team works closely across Account Executives, Product, Solutions, partners, and customer stakeholders to:
- Solve complex monetization and billing challenges
- Deliver tailored technical strategies, demos, and proofs of concept
- Support customers from pre\-sales through post\-sale technical health and adoption
- Surface customer insight that shapes roadmap and solution direction
- Help customers scale modern consumption and AI\-native business models
This is a highly collaborative environment for someone who wants to go beyond standard demos and play a meaningful role in shaping solution architecture, customer outcomes, and long\-term growth.
### 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 $136K-$177K 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 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 in Demand for This Role
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 ($156K) sits 28% below the category median. Disclosed range: $136K to $177K.
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.
Zuora AI Hiring
Zuora has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $177K - $177K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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