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About This Role
Madhive is the leading independent and fully customizable operating system built to help local media professionals build profitable, differentiated, and efficient businesses. Madhive empowers sales teams to extend their reach into streaming and connects local advertisers with the communities they serve. Madhive’s platform provides the unique ability to reach local audiences at national scale, with premium supply partnerships and end\-to\-end tools for planning, targeting, and measuring full\-funnel campaign outcomes. Powering campaigns for over 30,000 small and medium businesses per day, Madhive is driving the evolution of local media.
AI Enablement Director
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Madhive is seeking an AI Enablement Director to drive internal transformation by weaving AI into the fabric of how our teams work. This is a builder\-operator role designed to lift the work our people do by shifting focus from manual drudgery to high\-leverage strategic output. You will listen to functional needs, build load\-bearing V1 automations, and partner with leaders to drive lasting adoption and measurable productivity gains across teams.
This is a hybrid role, working 3 days a week at Madhive HQ in the Financial District of New York City.
Key Responsibilities
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- Design and ship AI\-enabled workflows and cross\-cutting tools for technical and non\-technical teams.
- Establish fluency standards and practical benchmarks for AI usage, productivity, and organizational impact.
- Lead employee AI training and adoption programs in close partnership with the People and L\&D teams.
- Develop a portfolio of internal load\-bearing applications and AI integrations, transitioning successful solutions to functional owners for long\-term operations.
- Serve as a change agent, leading adoption by visibly using tools and winning over stakeholders through measurable gains.
- Run discovery with functional leaders and define measurable success criteria for each workflow before building.
- Integrate automations with the systems teams already work in — CRM, ticketing, and workflow tools such as Salesforce, Jira, or equivalents — resolving data\-readiness and permissions blockers so solutions are production\-ready, not experimental.
- Drive adoption in: build trust, reduce friction, and ensure shipped solutions are actively used, with human review queues and audit trails where outputs need oversight.
What You'll Bring
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- Technical Craft: 8\-12 years of experience blending hands\-on building with driving tool adoption. You are technical enough to prototype workflows and wire up agentic coding tools (e.g., Claude Code, Cursor).
- Change Leadership: A proven track record of driving adoption of new ways of working across diverse teams with documented evidence of impact.
- Collaboration: Low\-ego and highly collaborative; able to sit with function leaders to translate real business needs into load\-bearing solutions.
- Mindset: Grit in ambiguity and a rigorous approach to measurement. You frame work as elevation and capacity building, never headcount reduction.
- Production Judgment: You ship to a reliability, latency, and observability bar appropriate for daily business use, and operate with high agency under ambiguity — scoping work, sequencing delivery, and removing blockers independently.
How We Measure Success
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- Workflow Multiplier (North Star): Measurable increase in workload\-per\-person on targeted workflows. The goal is to automate entire workflows so a team handles materially more volume — deals, tickets, analyses, reports — without added headcount, not to shave minutes off discrete tasks.
- Efficiency Gains: Tangible reduction in time\-to\-produce and manual people\-hours per outcome across teams.
- Employee Morale: Qualitative and quantitative shifts in sentiment as employees move out of drudgery into more strategic, energizing work.
Location / Work Model
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Hybrid, NYC – 3 days in office, with travel to other Madhive locations as needed.
The approximate compensation range for this position is $200,000\-$225,000\. The actual offer, reflecting the total compensation package and benefits, will be determined by a number of factors, including the applicant's experience, knowledge, skills, and abilities, as well as internal equity among our team.
\#LI\-Hybrid
We are Madhive
Madhive is a dynamic, diverse, innovative, and friendly place to work. We embrace our differences and believe they fuel our creativity. We come from varied backgrounds and think that’s important. Whether it’s taking ideas from previous lives and applying them in different ways or creating something completely new, we are all trail\-blazing team players who think big and want to make an impact.
We are committed to cultivating a culture of inclusion and collaboration. We welcome diversity in education, culture, opinions, race, ethnicity, gender identity, veteran status, religion, disability, sexual orientation, and beliefs.
Please be advised that we will NOT be using third\-party recruiting agencies for this search.
Salary Context
This $200K-$225K range is above the median 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 MadHive, 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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $200K to $225K.
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.
MadHive AI Hiring
MadHive has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $225K - $225K.
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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