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About This Role
Job Description
Position Summary
We are building an applied AI function focused on transforming how work gets done across the enterprise through agentic systems, workflow redesign, and intelligent data integration.
We are seeking a VP, AI to lead this transformation end\-to\-end.
This role owns how AI is identified, evaluated, built, and scaled within the organization’s workflows, moving from fragmented experimentation to a structured, repeatable system that delivers measurable business impact. You will operate across platforms, define how agents interact with enterprise systems and data, and establish the operating model required to scale adoption.
This is a systems leadership role requiring a balance of strategy, product thinking, and execution. You will translate ambiguous business problems into deployable solutions, drive platform and architecture decisions, and build the frameworks that embed AI into day\-to\-day operations.
Key Responsibilities
AI Strategy \& Business Impact
- Lead structured evaluation of AI platforms (e.g., Gemini, Claude, Perplexity), identifying strengths, limitations, and integration pathways
- Translate platform capabilities and constraints into clear prioritization for enterprise\-wide adoption
- Identify and scale high impact use cases tied to measurable business outcomes
Agentic Platform \& Workflow Ownership
- Design and scale multi\-step, agent\-driven workflows that automate and augment core business processes
- Translate complex, ambiguous workflows into structured, automatable systems
- Ensure AI is embedded into core operations , not deployed as isolated tools
- Establish reusable patterns to scale beyond one\-off solutions
- Drive consistency across teams while maintaining speed and flexibility
Enterprise Data \& Integration Strategy
- Define how third\-party systems (SaaS platforms, databases, APIs) integrate into AI workflows
- Establish scalable ingestion and integration patterns working with Infrastructure and Architecture Leadership throughout the organization (APIs, connectors, BigQuery, MCP, etc.)
- Ensure data is structured, accessible, and governed for AI consumption
Adoption \& Workflow Enablement
- Own adoption of AI\-driven workflows across the organization
- Ensure all AI initiatives are tied to clear, quantifiable outcomes
Drive initiatives from POC production* sustained usage
- Define and track success metrics, including:
- Workflow adoption
- Time saved / efficiency gains
- Throughput and decision velocity
- Business impact (cost, revenue, productivity)
Measurement \& Performance Ownership
- Define and track success metrics across all AI initiatives, including:
+ Decision velocity improvements
+ Productivity and output lift
- Establish baseline metrics prior to deployment and continuously measure post launch impact
- Ensure all AI solutions are tied to clear, quantifiable business outcomes
Workflow Transformation \& Operating Model
- Redesign business processes to embed AI into daily operations
- Establish and refine frameworks for intake, prioritization, and scaling of AI initiatives
- Track engineering velocity, output, and impact across workstreams
Technical \& Engineering Leadership
- Build and lead a high\-performing team of AI engineers and integration specialists
- Establish standards for agent design, orchestration, and integration
- Ensure high\-quality execution across POCs and production systems
- Partner with TPMs and engineering leadership to drive structured delivery
Cross\-Functional Leadership
- Act as the bridge between business, engineering, and platform teams
- Present clear, opinionated recommendations to senior leadership
- Drive alignment and best practices across the enterprise
- Engage with external partners (e.g., Google) to accelerate innovation and influence roadmap direction
Qualifications \& Experience
Required:
- 12\+ years of experience across AI, engineering, data, or technical product leadership
- Proven track record of deploying AI\-driven systems in enterprise environments
- Strong understanding of LLM/agent architectures, orchestration patterns (RAG, tool use, multi\-agent systems), and API\-driven integrations
- Experience making architecture decisions across AI platforms , including tradeoffs between RAG, direct API access, and hybrid approaches
- Experience translating ambiguous business problems into scalable system architectures and production\-grade AI solutions
- Demonstrated ability to lead cross\-functional teams and drive large\-scale initiatives
- Strong executive presence with the ability to influence senior stakeholders
What Success Looks Like
- AI is embedded into core workflows across the organization, not siloed tools
A repeatable system exists to move from idea build adoption* scale
- Agents and workflows are reusable, scalable, and integrated into real operations
- Enterprise data is governed, partitioned, accessible and usable in real time for AI\-driven decision\-making
- AI initiatives consistently deliver measurable business impact at scale
What Great Looks Like
- Brings clear, structured recommendations, not open\-ended questions
- Translates complex workflows into scalable systems, not one\-off solutions
- Makes strong platform and architecture decisions with clear tradeoffs
- Balances speed (POCs) with long\-term scalability
- Operates with full ownership across strategy, execution, and outcomes
\#LI\-KG1
\#LI\-HYBRID
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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
$240,000\.00 \- $290,000\.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 $240K-$290K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($265K) sits 23% above the category median. Disclosed range: $240K to $290K.
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.
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: $225K - $290K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 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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