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About This Role
Job Description
Job Description
The Vice President, Performance Marketing has ultimate accountability for leading a multi\-disciplinary team inclusive of a client portfolio comprised of key performance marketing initiatives. The ideal candidate will be accountable for leading a performance team responsible for programmatic, social, and YouTube/paid search efforts. The VP will have full responsibility for the team, capability, practices and client portfolio within that unit, including client relationships and satisfaction, practices and processes, and business development support. This individual will also be responsible for driving an integrated and seamless performance solution that supports their initiatives and positioning. This includes developing relationships with senior brand executives and the promotion of integrated client teams.
The ideal candidate should have a proven track record of success in complex business models and a strong understanding of a matrix organization. This individual must exhibit executive\-level knowledge across business development, client management, team building/management, and operations.
The VP, Performance Marketing will help to drive the overall Horizon strategy and practice within Horizon, and work to shape the future success of the organization. From this base, they will act as a senior steward of Horizon’s business, supporting culture, talent, and serving as a thought leader both within the company, as well as externally.
Main Duties and Responsibilities
Brand Integration and Support – 20%
- + Accountable for delivering best in class end to end performance solutions that reflect the market positioning and culture of Horizon
+ Develop and implement integration strategies to promote the integration of the embedded Horizon Performance Unit across strategy, planning, analytics and activation with the larger brand
+ Drive growth through capturing untapped organic performance opportunities within brand
+ Maintain market leading capability and best in class talent by promoting the Horizon performance culture and mindset by leveraging Horizon network resources and expertise
Account Management – 10%
- + Ultimate accountability over a client portfolio, driving client satisfaction, performance and engagement expansion
+ Continuous delivery of flawless execution, maintaining a strategic perspective to serve client needs and drive client results
Client Leadership – 10%
- + Serve as the executive steward across the book of business, building executive\-level relationships
+ Maintain a strong understanding of marketplace conditions relevant to the client and use that knowledge for ongoing communications
Practice Development – 20%
- + Ensure continuous implementation and advancement of practices, processes and tools that drive operational excellence and innovation across performance capabilities
+ Identify and develop product adjacencies to grow the business and expand relationships
Team Leadership \& Development – 20%
- + Drive a culture based on teamwork, collaboration with accountability, and intellectual curiosity
+ Manage and coach team members working to drive client business objectives and the strategic vision of Horizon’s Performance Media practice
+ Ensure adoption of strategic management through example setting, positive reinforcement, and behavioral correction
+ Own the hiring, training and mentoring of talent within the practice
+ Conduct performance reviews and career path plans for direct reports
New Business – 20%
- + Serve as executive business lead in appropriate business development opportunities in conjunction with the assigned pitch team; garner support from within
+ Identify key contributors from within the practice to participate in new business preparation
+ Continually support prospecting through personal and professional network
Supervisory Responsibilities
Manage and coach level team members *.*
Knowledge and Skills Required
- + Bachelor’s degree required.
+ Experience working within the digital space; experience specifically in digital media, marketing, or strategy.
+ 10\-15 years of experience.
+ Entertainment Client and/or agency experience required.
+ Proven success in delivering clear, strategic presentations to marketing executives and managing engagements.
Certificates, licenses and registrations
N/A
Physical Activity and Work Environment
N/A
The statements herein are intended to describe the general nature and level of work being performed by employees, and are not to be construed as an exhaustive list of responsibilities, duties and skills required of personnel so classified. Furthermore, they do not establish a contract for employment and are subject to change at the discretion of the employer.
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*Horizon Media is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.*
Salary Range
$195,000\.00 \- $235,000\.00
*A successful applicant’s actual base salary may vary based on factors such as individual’s skill sets, experience, training, education, licensure/certifications, and qualifications for the role.* *As an organization, we take an aptitude and competency\-based hiring approach.* *We provide a competitive total rewards package including a discretionary bonus and a variety of benefits including health insurance coverage, life and disability insurance, retirement savings plans, company paid holidays and unlimited paid time off (PTO), mental health and wellness resources, pet insurance, childcare resources, identity theft insurance, fertility assistance programs, and fitness reimbursement.*
Salary Context
This $195K-$235K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Horizon Media, Inc., 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 $166,983 based on 13,781 positions with disclosed compensation. This role's midpoint ($215K) sits 29% above the category median. Disclosed range: $195K to $235K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Horizon Media, Inc. AI Hiring
Horizon Media, Inc. has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Los Angeles, CA, US. Compensation range: $85K - $235K.
Location Context
AI roles in Los Angeles pay a median of $178,000 across 1,695 tracked positions. That's 3% below the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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