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About This Role
Job Description
Position Summary
We are pioneering a new AI development team dedicated to revolutionizing our agency's operations from the ground up. We are looking for an entrepreneurial and deeply technical AI Engineer to be a foundational member of this team.
This role sits at the intersection of AI engineering and real world execution.
As a Senior AI Engineer, you will design and build agentic systems that translate complex business workflows into automated, scalable solutions. You will work across multiple AI platforms, rapidly prototype solutions, and produce high\-impact solutions that create enterprise\-wide acceleration and efficiencies.
This role requires strong engineering fundamentals, product thinking, and the ability to operate in ambiguity.
Key Responsibilities
Agentic Solution Design \& Architecture
- Architect and deliver multi\-step, tool\-using agent systems that automate complex enterprise workflows end\-to\-end
- Partner with business leaders to identify high\-friction processes, define requirements, and translate ambiguity into structured, scalable solutions
- Decompose workflows into deterministic, automatable components and design agent architectures that drive enterprise\-wide efficiency and reuse
- Develop orchestration logic across APIs, data sources, and large language models, ensuring reliability, performance, and scalability
- Design around platform constraints, leveraging creative engineering approaches to unlock capabilities rather than being limited by them
Engineering Execution \& Platform Orchestration
- Build and deploy production\-grade systems, including RAG pipelines, tool integrations, and API\-driven workflows
- Rapidly prototype across AI platforms (e.g., Google Gemini, Claude, Perplexity AI) to validate feasibility and inform platform strategy
- Select and integrate the optimal models, tools, and ecosystems based on use case requirements, not platform bias
- Lead end\-to\-end development from zero\-to\-one prototypes through scaled, production\-ready systems
- Ensure solutions are extensible, modular, and designed for enterprise adoption
Workflow Transformation \& Stakeholder Integration
- Work directly with cross\-functional stakeholders to map workflows, align on outcomes, and operationalize AI\-driven transformation
- Translate business needs into technical architectures that deliver measurable impact on efficiency, throughput, and quality regularly optimizing for future scale
- Collaborate across product, design, data, and engineering teams to ensure solutions are usable, adopted, and outcome\-driven
Innovation \& Experimentation
- Operate at the forefront of the AI ecosystem, continuously evaluating emerging models, tools, and frameworks
- Lead rapid experimentation cycles across LLMs, RAG architectures, and agentic systems, iterating based on performance and user feedback
- Champion a culture of applied innovation, balancing speed, rigor, and scalability
- Drive the adoption of cutting\-edge capabilities to maintain competitive advantage and unlock new enterprise value
Qualifications \& Experience
Required:
- Proven experience architecting and deploying end\-to\-end AI/ML systems in production environments, with clear ownership from problem definition through delivery
- Deep hands\-on expertise in Google Cloud Platform, including production use of Vertex AI (Pipelines, Search, Model APIs) and the Gemini model family
- Demonstrated experience designing and building agentic AI systems, including multi\-step, tool\-using agents and complex RAG architectures; familiarity with frameworks such as LangChain, LlamaIndex, or similar
- Strong software engineering fundamentals, including:
+ Python proficiency and modern backend development (APIs, microservices)
+ Experience with containerization (Docker), orchestration (e.g., GKE), and CI/CD pipelines
+ Experience building LLM\-powered applications that integrate with external tools, APIs, and enterprise data sources
- Strong systems thinking mindset, with the ability to design around platform constraints rather than be blocked by them
- Demonstrated ability to operate in fast\-moving, ambiguous environments with a high degree of ownership and accountability
Nice to Have:
- Experience working across multiple AI platforms and systems (e.g., Gemini, Claude, Perplexity) and evaluating tradeoffs between them
- Experience integrating AI systems with enterprise tools and data platforms (e.g., Snowflake, BigQuery, SharePoint, Salesforce)
- Familiarity with enterprise security models, data access controls, and permission\-aware system design
- Background in client\-facing, consulting, or cross\-functional product environments
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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 \- $225,000\.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-$225K range is above 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. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $195K to $225K.
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
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