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About This Role
Job Description
Who We Are
Horizon Next is one of the industry's most innovative and data\-driven marketing organizations and sits at the intersection of three constantly changing landscapes: people, data, and media. Our business provides strategic leadership to accelerate growth for our clients through brand strategy, media planning and investment, and best in class analytics across all channels. As the leader in innovative business solutions, we are always pushing ourselves to understand what’s next: our next innovation, our next advancement in analytics, the market’s next media evolution, and your next breakthrough idea. Horizon Next operates with the single goal that tomorrow must outperform today.
At Horizon Next, we understand the value that different perspectives can bring to our clients and culture, so we strive for an environment where our employees feel welcomed, safe and empowered. We value YOU and believe that your authentic voice and unique perspective allows us to create a more rewarding culture, and experience, together.
Our simple recipe for success? We hire talented people (thinkers, doers, dreamers, makers), challenge them and give them every opportunity to grow.
What You’ll Do
Campaign Management \& Execution – 20%
- Supervise RFP creation and review of social partners, proposal analyses, and partner negotiations
- Oversee junior team campaign buying from set\-up to keyword selection through to targeting buckets, optimization, and pacing/reporting; implement QA processes
- Guide planner in campaign set\-up, including testing methodologies, reporting requirements, and optimization requirements, for ensuring full impact and efficiency of each campaign
- Ensure all trafficking and site tagging is accurate by junior team members
Social Media Strategy – 30%
- Lead strategic social plan development in tandem with the Digital \& Business Solutions teams
- Integrate social insights into overall planning process, incorporating data, research, and analytics into recommendations in order to deeply understand the role marketplace media plays in larger media objectives
- Partner closely with other Horizon Next teams (Business Solutions, Investment, etc.), building cross team relationships and incorporating relevant extensions
- Drive team brainstorms to kick off plan and consideration set development
- Develop POVs around relevant trends, tools, and emerging opportunities within paid social media
- Own creation and development of media specific documents such as media plans, objectives and strategies decks, and other related functions
Reporting \& Analysis – 10%
- Maintain oversight of junior team member’s data analysis and corresponding client feedback to provide recommendations and optimizations
- Actively drive optimization strategies, proactively communicating challenges and opportunities to both internal teams and clients
- Parse through large datasets to provide critical thinking and analysis, as well as draw conclusions and discover actionable implications
- Lead in development of processes with Horizon Next Analytics \& Business Intelligence teams to aggregate data and ensure standardization across Next accounts
- Oversee aggregation of data for dashboards/Excel trackers, as needed by account, ensuring accuracy and validity of data shown
- Determine set\-up, including testing methodologies, reporting requirements, and optimization requirements, for ensuring full impact and efficiency of each campaign
Relationship Management – 20%
- Own relationships with key partners during planning process, including Partners, Creative Agencies and Business Solutions team
- Take initiative in building relationships with other Horizon Next departments, i.e. Business Solutions, Traffic, Operations
- Apply knowledge of current/previous clients’ specific business and industry to enhance and further relationships
- Effectively build trust and establish positive relationships with clients
- Collaborate with Associate Director and Director to identify problems and recommend solutions
Team Management \& Supervision – 20%
- Manage and develop junior team member(s), creating growth plans and providing clear career goals
- Manage performance reviews, coaching to maximize success, identifying training needs for improved performance, setting goals for career development and ensuring actions are taken
- Provide education and training on best practices, media principals, and industry at large
- Participate in interview process for junior team members roles
- Guide team in setting goals and project tasks and timelines, ensuring team members have challenging, level\-appropriate projects and opportunities to learn new skills, contributing to overall team success
Who You Are
- A strong writer and presenter
- A left and right brain thinker – a data powered strategist
- An independent worker with strong time management and organization skills
- A problem solver with foresight and the ability to develop creative solutions
- Detail oriented with commitment to follow through
- Nimble and flexible to succeed in a fast\-paced environment
- A strong team player, willing to roll up your sleeves
- Interested in the social landscape and a desire to innovate and keep up with trends
- A supporter of and advocate for diversity, equity and inclusion
Preferred Skills \& Experience
- 3\+ years previous paid social media experience
- Thorough knowledge of advanced analytics and performance media
- Understanding of marketing principles, analytics and concepts
- Strong Microsoft Excel and Microsoft PowerPoint skills
- Experience buying ads on Facebook and Instagram is crucial, additional platforms preferred
- Exposure to advanced targeting/retargeting tactics in social
- Comfort owning and managing budget/investment levels in social media
Certificates, Licenses and Registrations
This role does not require certificates, licenses and/or registrations
Physical Activity and Work Environment
This role does not require any physical activity
*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.*
\#LI\-JC2
\#LI\-Hybrid
\#HN
*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
$90,000\.00 \- $105,000\.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 $90K-$105K range is below the median 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($97K) sits 42% below the category median. Disclosed range: $90K to $105K.
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 New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above 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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