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About This Role
Job Summary:
WHO ARE YOU?
Passionate and motivated. Driven, with an entrepreneurial spirit. Resourceful, innovative, forward thinking and committed. At Insomniac, our people embrace these qualities, so if this sounds like you then please read on!
WHO ARE WE ?
Insomniac produces some of the most innovative, immersive music festivals and events in the world. Enhanced by state-of-the-art lighting, pyrotechnics and sound design, large-scale art installations, theatrical performers and next generation special effects, our events captivate the senses and inspire a unique level of fan interaction. The quality of the Headliner experience is our top priority.
Insomniac produces 10,000 concerts, club nights and festivals for seven million attendees annually across the globe. Since its inception, Insomniac's events have taken place in 13 countries across five continents. The company's premiere annual event, Electric Daisy Carnival Las Vegas, is the world’s largest dance music festival and attracts more than 525,000 fans over three days. The company was founded by Pasquale Rotella and has been based in Los Angeles since it was formed in 1993.
THE ROLE
Insomniac Events is seeking a highly motivated and proactive Insomniac Legal Intern to join the legal team in Calabasas, CA. This position will focus on drafting various documents and performing research and analysis. This position reports to VP, Business & Legal Affairs. This is a paid internship and is not a remote position.
RESPONSIBILITIES
- Learn how laws and regulations are actively applied and implemented in a real-world setting
- Prepare, review/analyze, and finalize transactional documents
- Analyze issues pertinent to the development of production and programming
- Exposure to applied law while performing general research relating to our business
QUALIFICATIONS
- Must be at least 18 years of age
- Must be currently enrolled in at least second year of Law School
- Rising 2L or 3L of accredited law school with undergraduate or graduate course work related to contracts, copyright and entertainment/media law
- Previous industry experience prior to law school is a plus
- Typical commitment is 16-29 hours per week
- Must be motivated with an “Everything is possible” attitude
- Must be an active problem solver, instilled with a sense of urgency for projects large and small
- Knowledge of dance music and Insomniac’s brands
WORK ENVIRONMENT
- Must be willing to travel to work during holidays, evening and weekend hours, as required, to meet deadlines
- Must be able to tolerate loud noise levels and drastic temperature climates while working on site at various event location
- Must be able to work in open concept office space
Applicants for employment in the U.S. must possess work authorization, which does not require sponsorship by Insomniac for a visa.
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.
Insomniac 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, Insomniac 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, Insomniac 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. Insomniac 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.
Insomniac recruitment policies are designed to place the most highly qualified persons available in a timely and efficient manner. Insomniac may pursue all avenues available, including promotion from within, employee referrals, outside advertising, employment agencies, internet recruiting, job fairs, college recruiting and search firms.
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The expected compensation for this position in California is:
$19.00 USD Hourly
*\*\*Please note that the compensation information provided is a good faith estimate for this position only and is provided pursuant to the California Salary Transparency in Job Advertisements Law. It is estimated based on what a successful California applicant might be paid. It assumes that the successful candidate will be in California or perform the position from California. Similar positions located outside of California will not necessarily receive the same compensation. Insomniac takes into consideration a candidate’s education, training, and experience, as well as the position’s work location, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the California Law, a potential new employee’s salary history will not be used in compensation decisions.*
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The expected compensation for this position is:
$16.90 USD - $19.00 USD Hourly
*\*\* Pay is based on a number of factors including market location, qualifications, skills, and experience.*
Salary Context
This $33K-$39K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $154,000 based on 8,743 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $85,000. This role's midpoint ($36K) sits 76% below the category median. Disclosed range: $33K to $39K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Live Nation AI Hiring
Live Nation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Calabasas, CA, US. Compensation range: $39K - $39K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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