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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
TicketManager is building its AI and automation infrastructure from the ground up, and we are looking for a partner to build it with. As an AI Automation Engineer, you will work directly with the VP of Enterprise Technology & AI Strategy to turn AI strategy into working systems: n8n workflows, MCP servers, LLM integrations, chat and voice agents, and AI-driven engineering automation that makes the whole team faster.
This is not a role with a fully written roadmap. Requirements are fluid, projects range from a one-afternoon automation to a multi-month platform build, and the technology changes monthly. You will break things and fix them faster, stay endlessly curious, and set your own priorities. When the first three approaches fail, you find the fourth, because we believe there is always a way. Your work will directly shape how TicketManager operates, serves customers, and scales.
Responsibilities:* Build, maintain, and measure workflow automations in n8n across CRM, ERP, support, and fulfillment systems, from first prototype through stable production
- Build, maintain, and measure custom MCP servers that connect Claude and other models to internal systems and data
- Build and manage integrations with Claude, Gemini, and other model APIs, including prompt engineering and structured outputs
- Design agentic workflows where AI takes multi-step actions autonomously, and instrument them so we know when they work and when they don't
- Build and maintain voice and chat agents for sales and support, and own the development of a full in-app chatbot
- Use AI CLI tools such as Claude Code and Codex daily, and automate code review, testing, QA, and release pipelines
- Work with data lakes on AWS and Azure to keep AI systems fed with clean, current data, with security (credential handling, access scoping, audit logging) as a default
- Define success metrics before every pilot, track cost, performance, and adoption, and kill what doesn't work fast
- Evaluate new models and tools as they appear, and recommend when to adopt, switch, or skip
- Present your work to leadership and cross-functional teams, make the case for the technologies you believe in, and help non-technical colleagues put them to use
Desired Skills and Experience:* Bachelor's Degree required; MS in Computer Science, Engineering, MIS, or a relevant field is preferred
- 1-3 years of hands-on experience in automation engineering, systems integration, or a closely related technical role
- Hands-on experience with AI CLI tools such as Claude Code, Codex, or similar, and with code automation and AI-assisted code review and QA
- Real experience with n8n, Make, Zapier, or comparable orchestration tools, plus working knowledge of REST APIs, webhooks, and JSON
- Familiarity with LLM APIs (Claude, Gemini, or similar) and prompt engineering fundamentals
- A track record of solving problems nobody handed you a clean answer for; comfortable with fluid requirements and setting your own priorities
- Strong communication skills: you can present an idea and persuade others to adopt a technology or approach
- Visible evidence of constant learning, such as side projects and experiments you built to teach yourself
- Nice to have: experience with MCP (Model Context Protocol), or building chat and voice agents (VAPI, Deep Chat, assistant-ui, or similar)
- Nice to have: Python or JavaScript, CI/CD and release automation (GitHub Actions or similar), AWS or Azure data lakes, RAG and vector databases
TicketManager Highlights:* Location: Calabasas, CA (HQ)
- Compensation: $95,000 - $125,000 base salary + Variable Compensation
- Reports to: VP, Enterprise Technology & AI Strategy
- 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 $95K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $95K to $125K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TicketManager AI Hiring
TicketManager has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Calabasas, CA, US, New York, NY. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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