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About This Role
Live events are fun.
Concerts, sporting events, and festivals create memorable lifelong experiences with clients, prospects, partners, friends, and family—and they drive real business impact.
Companies spend more than $600 billion each year on client entertainment. TicketManager is the enterprise platform they trust to source, manage, track, and report on live event tickets and hospitality—while maintaining compliance, control, and visibility across the organization.
Built at the intersection of live events, technology, and enterprise operations, TicketManager makes client entertainment easy and measurable. Our industry\-leading software supports everything from invitations and event execution to post\-event reporting, helping companies clearly prove ROI and strengthen relationships through unforgettable experiences.
TicketManager is trusted by thousands of global brands, including Anheuser\-Busch, Verizon, American Express, Nike, and Visa, as well as Fortune 500 companies, professional sports leagues, and major organizations such as the NBA, NFL, NCAA, and NASCAR.
We’re also proud to partner with some of the most iconic teams and venues in sports and entertainment, including the New York Jets, Philadelphia Eagles and Phillies, Capital One Arena, Texas Rangers, LAFC, and Seattle Seahawks, along with more than 50 professional and collegiate teams, franchises, universities, and technology providers.
Why Work at TicketManager
At TicketManager, you’ll help build technology that brings people together through live experiences—while solving complex, real\-world problems for the world’s best companies. We’re a fast\-growing, profitable company that values ownership, collaboration, and excellence, and we’re building a team that’s passionate about making work impactful and fun.
If you’re excited about live events, cutting\-edge technology, and doing work that truly matters, you’ll feel right at home here.
The Role
We are seeking an AI Revenue Operations Manager (Strategy \& Innovation) to help architect and scale the next evolution of TicketManager’s go\-to\-market engine.
This role sits at the intersection of strategy, systems, and AI innovation, responsible for aligning Sales, Marketing, and Customer Success while embedding automation and intelligence across the revenue lifecycle.
You will act as both a strategic operator and builder—owning processes, data, and technology that drive predictable revenue growth, while identifying and deploying AI solutions that unlock efficiency and competitive advantage.
This is a high\-impact role influencing executive decision\-making and directly shaping how TicketManager scales.
Responsibilities:
- Revenue Strategy \& GTM Alignment
+ Partner with leadership to define and execute revenue strategies across Sales, Marketing, and Customer Success
+ Align funnel definitions, KPIs, and forecasting models to ensure consistency and visibility across all revenue teams
+ Translate business goals into scalable operational plans, workflows, and dashboards
+ Drive cross\-functional alignment to eliminate silos and improve pipeline efficiency
- AI Strategy \& Innovation
+ Identify, evaluate, and implement AI use cases across the GTM funnel (lead scoring, personalization, forecasting, automation)
+ Build and deploy AI\-driven workflows leveraging CRM automation, enrichment platforms, and LLM\-based solutions
+ Partner with Product, Engineering, and Marketing to integrate AI into customer\-facing and internal systems
+ Continuously test, iterate, and optimize AI\-driven solutions to improve performance and ROI
- Systems, Data \& Automation
+ Own and optimize the GTM tech stack (CRM, marketing automation, enrichment, analytics tools)
+ Ensure data integrity, governance, and a single source of truth across all systems
+ Design and implement scalable workflows to reduce manual work and increase productivity
+ Evaluate and integrate new tools to enhance automation and operational efficiency
- Analytics, Reporting \& Insights
+ Build and maintain dashboards tracking pipeline health, conversion rates, ARR, and campaign performance
+ Analyze funnel performance to identify opportunities for growth and optimization
+ Deliver actionable insights to leadership to inform strategic decision\-making
+ Develop forecasting models and scenario planning frameworks
- Process Optimization \& Execution
+ Map, audit, and continuously improve end\-to\-end revenue processes
+ Standardize workflows across teams to drive consistency and scalability
+ Lead cross\-functional initiatives to improve conversion rates and reduce friction in the customer journey
+ Establish and maintain operational best practices and documentation
Desired Skills and Experience:
- Bachelor’s Degree Required
- 5–8\+ years of experience in Revenue Operations, Sales Operations, Marketing Operations, or similar roles within B2B SaaS or high\-growth technology environments
- Proven track record of driving revenue growth through systems, data, and process optimization
- Hands\-on experience managing and optimizing CRM and GTM tech stacks (Microsoft Dynamics 365 preferred)
- Experience working with AI tools and automation platforms (e.g., enrichment tools, workflow automation, LLM\-based solutions)
- Strong understanding of data architecture, integrations, and scalable automation frameworks
- Advanced analytical skills with experience building dashboards, reporting, and forecasting models
- Ability to translate complex data into actionable insights and strategic recommendations
- Experience leading cross\-functional initiatives and influencing stakeholders at all levels, including executive leadership
- Demonstrated curiosity and applied experience with AI\-driven workflows, automation, or agent\-based systems within GTM or business operations
- Strong communication skills with the ability to simplify complex technical concepts for business audiences
TicketManager Highlights:
- Location: Calabasas, CA (HQ) or New York, NY
- Compensation: $100,000\-$160,000 Base Salary \& Bonus Eligibility
- Reports to: Revenue Operations
- Work Expectations: Role is In\-Office, Monday\-Friday
- Retirement: 401(k) \& Company Match
- Health Benefits: Medical, Dental, Vision \& Chiropractic
- Time Off: Unlimited PTO
- Interview Process: Multistage interview process with senior leaders across TicketManager to ensure strong alignment on role scope and expectations.
- Events: Quarterly live event credits (we practice what we preach!), monthly happy hours, and volunteering
- Perks: Fun, collaborative, in\-office culture at our HQ with catered lunches and big company perks with the autonomy of a high\-growth startup.
- Recognition: Inc. 5000 fastest\-growing private company by Inc. Magazine six years in a row. Recognized as one of the Best Places to Work by Inc. Magazine, The LA Business Journal, and Sports Business Journal
- 4\.5 out of 5 Glassdoor rating
- Used by over 4,000 globally known companies including \~15% of the Fortune 500
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Salary Context
This $100K-$160K range is in the lower quartile 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 TicketManager, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($130K) sits 40% below the category median. Disclosed range: $100K to $160K.
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.
TicketManager AI Hiring
TicketManager has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $160K - $160K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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