Interested in this AI/ML Engineer role at Horizon Media, Inc.?
Apply Now →Skills & Technologies
About This Role
Job Description
Who We Are
Horizon Media , founded in 1989 by Bill Koenigsberg, is recognized as one of the most innovative marketing and advertising firms. We are headquartered in New York City, with offices in Los Angeles and Toronto. A leader in driving business solutions for marketers, Horizon is known for its highly personal approach to client service. Renowned for its incredible culture, Horizon is consistently named to all the prestigious annual Best Places to Work lists published by Fortune, AdAge, Crain’s New York Business and Los Angeles Business Journal. Together we are building a place of belonging.
At Horizon, 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 Is blu.
blu. is Horizon’s omni\-marketing capability that is powered by a Connected Marketing Platform, built in\-house from the ground up. The foundation of this platform is an Identity Framework built upon our relationship with TransUnion, whose PII data acts as our identity spine. By integrating with agency partners who have deterministic data assets we have licensed and attached 11K\+ attributes to bridge the gap between martech and adtech to deliver a true 360 consumer view. This enables brands to engage customers and prospects across all journeys. Our portfolio of solutions and products are designed to complement a clients’ existing tech.
Proprietary insights tools enhance profiles with psychographic attributes and custom models identify and assign propensity against audiences based on KPIs. We have a central, connected, user\-friendly platform for insight development, audience creation and activation. Our Solutions \& Services are built on top of the Connected Marketing Platform offering bespoke capabilities across business strategy, planning, activation, measurement and optimization.
Job Summary
The Associate Director, Marketing Sciences is at the center of data, media and brand strategy. They act as a day\-to\-day contributor on specific client teams and focus on the generation of insights to inform audience strategy for planning and analysis of performance and optimizations while campaigns are still inflight. Key responsibilities are working with big data (data extraction \& transformation) as well the application of advanced statistics and machine learning techniques to accomplish a variety of innovative and investigative projects on behalf of Horizon agencies and their clients. The role requires hands\-on analytics expertise and client\-facing solution design and will influence Horizon data strategy and product direction.
This role often requires an ability to work cross\-functionally with Client Architects and other teams key to data acquisition through client presentation, as a result, a collaborative spirit and strong communication skills are required. They will be expected to become expert in data assets within Horizon’s proprietary Connected Marketing Platform called blu. and take ownership of specific data sources and client accounts. This role will report to a Vice President, Marketing Science, blu.
Responsibilities
15% Product Innovation \& Management
- Operate as a Product Innovator and become a Product expert and power user, to drive continuing improvement in both in the analysis and results being supported by AI and the continuing custom solutions built on top and around those
- Experience with the ongoing operation and support of a Product in development and/or production
- Ability to ensure design principles that scale and can be repeatable and/or extensible to other clients or industries
- Experience with release management processes, including all levels of testing (Alph, Beta, QA, UAT)
- Ability to define features, models, queries and requests and provide “proof of concept”s for Product enhancement and future incorporation into blu\+ product
40% Solutions Design \& Innovation
- Ability to understand and leverage blu. structured and unstructured data
- Data transformation/mining and generation of insights to explain audience performance and inform optimization
- Data join process between blu audiences, media exposure and conversion events to build feedback loops
- Analysis of audience conversion to KPIs
- Model creation to estimate contribution of attributes driving conversion
- Validation of models used during the Audience Building process
- Assessment of audience duplication and incrementality by channel, partner, or tactic
- User/log level data extraction from delivery sources to create new audiences based on exposure to manage frequency
- Database(s) querying to extract exposure events and create a new audience based on exposure by paid media channel (digital or linear)
- Problem solving and development for new modeling and analysis techniques
- Increasingly utilize AI solutions to inform and expedite some or all of the required analysis
40% Technical \& Analytical Excellence
- Advanced knowledge of dimensions \& metrics (people based, behavioral and media data)
- Ability to understand and make sense of the relationships/connections across datasets
- Knowledge of the data dictionaries and taxonomies of structured data sets
- Experience in ML and different techniques of supervised and unsupervised learning ETL \& EDA processes
- Understanding of the complexity of working with Big Data
5% Leadership \& Team Management
- Act as a collaborative partner to the business and all stakeholders
- Participate in daily standups and meet delivery commitments
- Proactively identify and, when required, escalate issues and blockers striving to avoid business/client impact
- Work with Client Architects and Data Solutions to outline work required to fulfil requests and generate outputs
- Provide input to team training and development needs
Qualifications
- BS/BA degree in Computer Science, Statistics, Applied Mathematics, or a related field required. Advanced Statistics a plus.
- 3\+ years relevant experience, preferably in a media, marketing or digital advertising environment.
- Working knowledge of Big and basic data mining routines a must.
- Expert knowledge of SQL/Python /R knowledge a must.
- Knowledge of GitHub a plus.
- Machine Learning a plus.
- Solid understanding of data technology integrations across data sources and ecosystems, and ability to troubleshoot.
- Experience working with digital analytics and audience insights reporting (Campaign Manager/ GA/ Adobe/GA360\) \- a plus.
- Ability to work in a fast paced, multiple project environment on an independent basis and with minimal supervision.
- A team player who can work collaboratively within the group and across business units/functions.
- Excellent organizational, communication and interpersonal skills.
\#LI\-KG1
\#LI\-HYBRID
\#HM
*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
$125,000\.00 \- $165,000\.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 $125K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $125K to $165K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Horizon Media, Inc. AI Hiring
Horizon Media, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $165K - $225K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.