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About This Role
Job Summary:
JOB DESCRIPTION – PRINCIPAL PLATFORM ENGINEER, AI \& AUTOMATION
Location: Phoenix, Arizona (Hybrid)
Division: Ticketmaster NA
Line Manager: Director, Software Engineering
Contract Terms: Permanent, full\-time/40h per week
THE TEAM
The Global Host Platform group designs, builds, and runs the foundational systems powering Ticketmaster’s concert sales. Our Host Platform Engineering team is a lean, high\-leverage group of experts who run our platform at global scale. We write, build, and maintain the internal tools that sustain the scale and resiliency we require to meet peak demand during high\-demand onsales.
THE JOB
We’re looking for a Principal Platform Engineer, AI \& Automation to join our Host Platform Engineering team within the Global Host Platform org. This is a role for a seasoned engineer who wants to reduce tech debt, enable teams with automation and AI, and raise the bar for operational excellence.
You’ll work across systems that power some of the most business\-critical parts of live entertainment. Working alongside AI agents and assistants, you’ll rapidly deliver hands\-on code, build scalable automation, and lead the adoption of agentic, AI\-driven SRE and DevOps tools to deliver resiliency and security in today’s constantly evolving platform engineering landscape. This is a chance to shape how a high\-impact engineering team delivers measurable value with AI continually enhancing our capabilities in new ways.
WHAT YOU WILL BE DOING
Software Engineering \& Automation
- Write high\-quality, maintainable code that accelerates platform automation and reduces tech debt across Global Host Platform systems.
- Build the internal tools that keep our systems scalable and resilient at global scale — engineered to hold up under the peak load of high\-demand onsales.
- Use scripting (Python, Bash) and infrastructure\-as\-code (Terraform, Ansible) to simplify and standardize operational workflows.
- Lead technical deep\-dives to spot automation opportunities and tackle long\-standing inefficiencies.
AI\-Driven Development
- Drive adoption of AI developer tools — e.g. Claude Code, GitHub Copilot, Cursor, Amazon Q, and local models via Ollama — across the team.
- Design and champion agentic AI workflows that plan, reason, and act — building frameworks for autonomous task execution and tool chaining, including via the Model Context Protocol (MCP).
- Define and evolve our internal patterns for LLM\-backed development, spec\-driven workflows, and AI\-augmented refactoring — with attention to code quality, security, and human review.
Team Enablement \& Standards
- Define and evangelize internal standards for platform automation, SRE practice, and code quality.
- Mentor engineers and spread knowledge across the team, especially on getting real leverage from modern AI tooling.
- Partner with reliability and platform engineers to deliver automation that sticks — measurable impact over buzzwords.
- Lead in\-person team trainings, gatherings, and hackathons on\-site monthly, and travel quarterly.
WHAT YOU NEED TO KNOW (or TECHNICAL SKILLS)
- 8\+ years of hands\-on software engineering experience; systems or backend engineering preferred.
- Hands\-on experience with AI developer tools (Claude Code, GitHub Copilot, Cursor, etc.), LLM\-backed scripting, and agentic AI systems capable of planning, tool use, and autonomous task execution in engineering workflows.
- Agentic AI in both our developer processes and our production services is a must — you build with it day\-to\-day and you run it where reliability actually counts.
- Strong command of multiple scripting languages and infrastructure\-as\-code tooling.
- Experience with automated VM deployments (VMware) and automated configuration management (Ansible).
- Containerizing software and tools (Docker, Podman) and deploying them in orchestrated environments (Kubernetes).
- Experience in SRE, DevOps, or platform engineering — or the curiosity and track record to ramp up fast.
- A genuine drive to reduce tech debt, enable teammates, and automate the annoying stuff.
- Ability to lead a team in blending software engineering best practices with fast\-moving AI capabilities — and to keep that blend current as both continue to evolve.
- A habit of keeping pace with a market that moves weekly, not yearly — and the instinct to lead and teach others so the whole team stays current, not just you.
- Excellent communication and collaboration skills; you enjoy being a multiplier.
Nice to have
- Background in high\-scale production systems (AWS, observability platforms, etc.).
- Experience building or operating MCP servers, or integrating AI agents with internal tooling.
- Experience introducing new development practices or tooling into an existing engineering org.
- Prior involvement in incident management, disaster recovery, or reliability strategy.
YOU (BEHAVIOURAL SKILLS)
- Work well across teams — you communicate clearly, share context, and collaborate effectively across disciplines to get things done.
- Take ownership — you don’t wait for permission to fix what’s broken. You identify problems, propose solutions, and follow through.
- Lead from the front – you research and adopt emerging AI quickly and voluntarily, balancing the drive to learn what's new with consistently delivering business value now
- Think in systems — you design for scale, reliability, and maintainability. You simplify where possible and automate where it counts.
- Move with intent — you prioritize impact over perfection, ship iteratively, and measure results.
- Push tooling forward — you adopt and advocate for tools that make engineering faster and smarter, especially AI\-driven ones.
- Raise the bar — you care about code quality, operational excellence, and leaving things better than you found them.
BENEFITS \& PERKS
Through our ‘Taking Care of Our Own’ program, we provide benefits across six key pillars:
- HEALTH : Medical, vision, dental and mental health benefits for you and your family, with access to a health care concierge, and Flexible or Health Savings Accounts (FSA or HSA)
- YOURSELF : Free concert tickets, generous paid time off including paid holidays, sick time, and personal days
- WEALTH : 401(k) program with company match, stock reimbursement program
- FAMILY : New parent programs including caregiver leave, plus fertility, adoption, foster, or surrogacy support
- CAREER : Career and skill development programs with School of Live, tuition reimbursement, and student loan repayment
- OTHERS : Volunteer time off, crowdfunding match
LIFE AT TICKETMASTER
We are proud to be a part of Live Nation Entertainment, the world’s largest live entertainment company.
Our mission at Ticketmaster is to connect people around the world to the live events they love. Ticketmaster is the world’s largest ticket marketplace and the global market leader in live event ticketing products and services. Through official partnerships with thousands of venues, artists, sports teams, festivals, performing arts centers and theaters, Ticketmaster processes 550 million tickets per year across 35\+ different countries.
We do it all with an intense passion for Live and an inspiring and diverse culture driven by accessible leaders, attentive managers, and enthusiastic teams. If you’re passionate about live entertainment like we are, and you want to work at a company dedicated to helping millions of fans experience it, we want to hear from you.
Our work is guided by our values:
Reliability \- Fans and clients count on us to power their live event experiences and we rely on each other to make it happen.
Teamwork – While we celebrate individual achievements, we know have more success as a team.
Integrity \- We are committed to the highest moral and ethical standards on behalf of the countless partners and stakeholders we represent.
Belonging \- We are committed to building a culture in which all people can be their authentic selves, have an equal voice and opportunities to thrive.
EQUAL EMPLOYMENT OPPORTUNITY
We aspire to build teams that reflect and support the fans and artists we serve. Every day we aim to promote environments where everyone can be themselves, contribute fully, and thrive within our company and at our events. As a growing business we will encourage you to develop your professional and personal aspirations, enjoy new experiences, and learn from the talented people you will be working with.
Ticketmaster strongly supports equal employment opportunity for all applicants regardless of age (40 and over), ancestry, color, religious creed (including religious dress and grooming practices), family and medical care leave or the denial of family and medical care leave, mental or physical disability (including HIV and AIDS), marital status, domestic partner status, medical condition (including cancer and genetic characteristics), genetic information, military and veteran status, political affiliation, national origin (including language use restrictions), citizenship, race, sex (including pregnancy, childbirth, breastfeeding and medical conditions related to pregnancy, childbirth or breastfeeding), gender, gender identity, and gender expression, sexual orientation, intersectionality, or any other basis protected by applicable federal, state or local law, rule, ordinance or regulation.
We will consider qualified applicants with criminal histories in a manner consistent with the requirements of the Los Angeles Fair Chance Ordinance, San Francisco Fair Chance Ordinance and the California Fair Chance Act and consistent with other similar and / or applicable laws in other areas.
We also afford equal employment opportunities to qualified individuals with a disability. For this reason, Ticketmaster will make reasonable accommodations for the known physical or mental limitations of an otherwise qualified individual with a disability who is an applicant consistent with its legal obligations to do so, including reasonable accommodations related to pregnancy in accordance with applicable local, state and / or federal law. As part of its commitment to make reasonable accommodations, Ticketmaster also wishes to participate in a timely, good faith, interactive process with a disabled applicant to determine effective reasonable accommodations, if any, which can be made in response to a request for accommodations. Applicants are invited to identify reasonable accommodations that can be made to assist them to perform the essential functions of the position they seek. Any applicant who requires an accommodation in order to perform the essential functions of the job should contact a Human Resources Representative to request the opportunity to participate in a timely interactive process. Ticketmaster will also provide reasonable religious accommodations on a case\-by\-case basis.
HIRING PRACTICES
The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required of employees assigned to this job.
Ticketmaster recruitment policies are designed to place the most highly qualified persons available in a timely and efficient manner. Ticketmaster may pursue all avenues available, including promotion from within, employee referrals, outside advertising, employment agencies, internet recruiting, job fairs, college recruiting and search firms. Ticketmaster may use artificial intelligence (AI) tools to support application screening and assessment. All hiring decisions are made with human review.
Live Nation Entertainment will never request payment or equipment purchases as part of the hiring process. Recruiters will only contact candidates from official Live Nation or affiliated brand email domains.
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 Live Nation, 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.
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.
Live Nation AI Hiring
Live Nation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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