Interested in this AI/ML Engineer role at eMarketer?
Apply Now →About This Role
EMARKETER is a subsidiary of Axel Springer SE, a family\-owned transatlantic media company headquartered in Berlin and New York. Axel Springer's guiding principles – first articulated as The Essentials by founder Axel Springer in the aftermath of World War II – remain a cornerstone of the company's foundation today. Learn more about Axel Springer.
We're hiring a Senior Writer/Analyst to lead our ad tech and AI content for our subscription newsletter team at EMARKETER.
The Role and Team:
EMARKETER's Briefings are a suite of premium subscription newsletters reaching tens of thousands of business professionals daily. We are seeking a Senior Writer/Analyst \- a high\-profile industry authority with a distinct point of view to write daily content for our Tech, AI, and Marketing \& Advertising Briefings.
In this role, you will deeply own the ad tech beat, demystifying the economics of programmatic transactions, ad measurement, and consumer data, while also covering broader consumer tech and emerging tech trends. That's everything from how AI, CTV, AR/VR, social media and wearables developments are reshaping marketing strategies, content creation, and customer engagement. You will translate complex market shifts into sharp, data\-backed, and actionable takeaways for an executive audience. Beyond writing, you will serve as a subject matter expert and represent EMARKETER at industry conferences, webinars, and client events, building a reputation as a trusted, data\-driven expert on the industry and a go\-to resource both internally and externally. This position is ideal for a highly analytical writer who is ready to put a stake in the ground on industry predictions and bridge the gap between deep\-dive research and high\-impact daily media.
This is a hybrid role based out of our New York City office, with regular in\-office expectations.
Applicants must be authorized to work in the United States without the need for visa sponsorship, now or in the future. Candidates must be within commuting distance of our New York City office. Relocation assistance is not available.
The Ideal Candidate is:
- An analyst or practitioner who understands the landscape and dynamics of ad tech, consumer tech, and AI and is knowledgeable about the broader advertising market.
- An exceptional communicator who can identify the "so what" in business news and translate high\-level concepts into high\-impact, concise, and actionable insights.
- A visual storyteller with a sharp eye for data and the ability to translate complex data sets into compelling visualizations that anchor and elevate our stories.
- An innovator whochallenges the status quo, pilots new formats, and identifies untapped coverage opportunities.
- A data\-focused analyst with the ability to quickly sort through masses of data, find interesting trends, and convey them in a digestible way.
- A team player who thrives in a fast\-paced and rapidly changing collaborative environment.
Key Responsibilities:
- Pitch, research, and produce insightful and data\-driven content in daily newsletter products on the intersections of tech, AI, and marketing. \~10 articles per week.
- Create forward\-looking original insights to guide client decision\-making on trends, disruptions and challenges.
- Create engaging data\-centric charts and graphics that support your analysis and help tell stories.
- Ensure all deliverables include sharp data analytics, meet our high quality standards, publish on schedule, and provide actionable insights that help clients get their jobs done.
- Deliver tightly structured, insightful and actionable reports (\~5\-6 per year) and presentations tailored to the key needs of our audience.
- Provide constructive peer feedback to fellow analysts across the marketing, media and ad tech team as they develop and produce stories.
- Engage proactively with the industry community through social media like LinkedIn, at major conferences, and in the media.
- Represent EMARKETER at conferences, webinars, and in media as an industry thought leader.
- Join sales calls on an ad hoc basis to talk about your research.
- Deliver on sponsored and paid presentations, webinars and podcasts as needed.
Desired Skills \& Experience:
- Background in a role involving writing and analysis centered on advertising or adtech (e.g., market research, data\-driven journalism, consulting); ideally with a few years of industry experience.
- Ability to work in a fast\-paced environment with daily deadlines and quick turnaround times.
- Public speaking experience with client presentations, industry events, media appearances and/or webinars.
- Expertise in creating and delivering memorable, story\-driven presentations.
- Ability to multitask and prioritize as assignments shift in a fast\-paced, deadline\-driven environment.
- Exceptional writing, charting, data comparison, and analysis skills.
#### Salary \& Benefits:
- Base salary: $100,000\-$110,000 (dependent on skills, experience, and competencies)
- Unlimited PTO, 10 paid holidays, and 16 weeks of parental leave
- Comprehensive medical, dental, and vision insurance plans
- Matched and vested 401k plan
- Access to resources for financial planning guidance, family planning services, mental health reach\-out, and Employee Assistance Programs (EAP)
- Additional benefits include commuter benefits, phone reimbursement, gym membership discounts, and more
About EMARKETER
EMARKETER is the world's leading research company focused on digital transformation. We hire people who are passionate about providing business leaders with actionable data and insights in the areas of digital marketing and advertising, media, retail and ecommerce, financial services, healthcare, and more. Our clients, who rely on our content to make informed decisions, include top global brands within Fortune 1000 companies, as well as smaller firms striving to compete in a digital age.
At EMARKETER, we pride ourselves on an inclusive work environment and continuously strive for diversity of thought, identity, and experience while encouraging growth and providing support to team members throughout the organization. EMARKETER is committed to corporate transparency through regular business updates and an always\-open line of communication.
What We Value
Our people are the foundation of our success. Guided by our values, we:
- Serve Our Clients: We prioritize their needs to deliver excellence in our products and services.
- Work as One Team: We collaborate with trust, accountability, and transparency.
- Innovate and Adapt: We foster curiosity, resilience, and fearless exploration of new ideas.
- Celebrate Diversity and Inclusion: We embrace a diverse, inclusive environment where all voices are valued and respected.
To learn more about what it's like to work at EMARKETER check out our careers page and life page.
*If this sounds like a great job for you, please apply online and tell us a bit about why you're a good fit for the role.*
*Please note that for all positions at EMARKETER, there is an exercise component specific to the role to reduce selection bias in our recruiting process and test how you apply knowledge.*
Salary Context
This $100K-$110K range is in the lower quartile 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 eMarketer, 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 in Demand for This Role
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. This role's midpoint ($105K) sits 52% below the category median. Disclosed range: $100K to $110K.
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.
eMarketer AI Hiring
eMarketer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $110K - $110K.
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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