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About This Role
Who is Nexxen?
Flexible advertising, unified by data. Nexxen empowers advertisers, agencies, publishers and broadcasters around the world to utilize data and advanced TV in the ways that are most meaningful to them. Our flexible and unified technology stack comprises a demand\-side platform (“DSP”) and supply\-side platform (“SSP”), with the Nexxen Data Platform at its core.
Why join the Nexxen team?
With a global footprint, you can be part of a team that is transforming advertising through our creative, flexible and unified solutions. Employees hustle, commit and dedicate themselves to pillars that make up the Nexxen Way – the 3Cs \- Customer Centric, Curious Mindset, Collaborative with No Ego.
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At Nexxen, we care about the well\-being of our current and future employees. We are aware of the growing number of online scams and fraudulent job postings, and we urge all job seekers to remain vigilant. Please be advised that Nexxen will never request payment (whether in cash, cryptocurrency, or any other form) as a condition of employment, offer positions that require you to invest in vague or dubious financial schemes, or promote roles that resemble get\-rich\-quick opportunities. If you receive a suspicious message claiming to be from Nexxen or encounter a questionable job posting associated with our name, please contact us at infosec@nexxen.com to verify its legitimacy. Your trust is important to us. Stay safe and informed.
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What You’ll Do:
As the Senior Staff AI Enablement, you will sit at the critical intersection of Nexxen’s operational workflows and emerging technologies to architect and execute a comprehensive automation and adoption strategy. You will lead the overarching approach for integrating AI tools across the organization, ensuring our teams from Client Services, Operations, Finance, HR, Sales, and Legal are equipped with efficient, scalable, and high\-performing processes to drive business growth.
You are a seasoned leader with deep expertise in process engineering, AI toolsets (LLMs, agents, and low\-code platforms), and cross\-functional change management. This is a high\-visibility role reporting into the COO, requiring you to operate as both a strategic architect of AI\-enabled systems and a hands\-on operator who builds AI fluency across a global workforce.
- Strategic Leadership: Define and execute the AI enablement strategy across all business lines, ensuring alignment with company\-wide efficiency, growth, and innovation goals.
- Process Optimization: Lead initiatives to streamline internal workflows by identifying manual friction points across departments and uncovering high\-impact automation opportunities.
- AI Implementation \& Tooling: Evaluate, select, and implement AI platforms (e.g., LLM\-based tools such as Claude, agents, and low\-code solutions) to improve task execution while maintaining strong data privacy, security, and cost efficiency.
- Hands\-on Enablement: Design and deliver tailored training sessions, workshops, and prompt engineering resources that enable teams to effectively integrate AI into their day\-to\-day workflows.
- AI Guild \& Community: Launch and lead Nexxen’s internal “AI Guild,” building a network of cross\-functional AI Champions who share best practices, pilot use cases, and accelerate adoption across the organization.
- Operational Excellence: Own the end\-to\-end performance and scalability of AI\-enabled workflows, leveraging low\-code tools to build and maintain production\-ready solutions that address real operational pain points.
- Product \& Engineering Collaboration: Act as the primary liaison between business teams and Product/Engineering, translating operational needs into technical requirements for integrations, automation, and system enhancements.
- Adoption Tracking \& ROI: Establish metrics and dashboards to track AI utilization, engagement, and productivity gains, providing leadership with clear visibility into adoption and business impact.
What You’ll Bring:
- BA/BS in Business, Computer Science, Engineering, or a related quantitative field.
- 8\+ years of experience in operations, digital transformation, or related fields, with a proven track record of driving AI implementation, automation, or process optimization initiatives.
- Strong, hands\-on familiarity with LLM\-based tools and automation frameworks, including APIs, webhooks, and low\-code platforms (e.g., Make, Zapier, n8n).
- Excellent interpersonal skills with the ability to influence stakeholders at all levels (from C\-suite leadership to individual contributors) and translate complex concepts into practical applications.
- Strong problem\-solving capabilities, with attention to workflow logic, data privacy considerations, and output quality.
- Ability to operate in a fast\-paced, ambiguous environment while building and scaling a new function from the ground up.
In support of pay transparency and equity, the minimum and maximum full\-time annual base salary for this role is $180,000 \- $260,000 the time of posting. While this is our reasonable expectation this is not a guarantee of compensation or salary, actual compensation is influenced by a wide range of factors including but not limited to skill set, level of experience, education, certifications, responsibility, and geographic location. Candidates hired to work in other locations will be subject to the pay range associated with that location. We offer a variety of benefits, including medical, dental, vision, disability insurance, 401(k), EAP, parental leave, unlimited vacation, and company\-paid holidays. The specific programs and options available will vary depending on the state, start date, and employment type. Our Talent Acquisition team will be happy to answer any questions you may have.
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Salary Context
This $180K-$260K range is above the 75th percentile 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 Nexxen, 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. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($220K) sits 32% above the category median. Disclosed range: $180K to $260K.
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.
Nexxen AI Hiring
Nexxen has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $135K - $260K.
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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