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About This Role
Who we are
Since 1843, The Economist Group has championed independence, excellence and openness, helping people understand and tackle the critical challenges shaping the world. Today, we are building on that legacy as a global media and information\-services company powered by digital innovation, analytical rigour and evidence\-based insight.
Across our three businesses \-*The Economist, Economist Enterprise and Economist Education* \- we deliver trusted analysis and insights to individuals and organisations in more than 170 countries. United by a shared purpose to drive progress, we empower decision\-makers to make sense of change and chart a course through an increasingly complex world.
As a colleague, you will be part of a culture that values ideas, encourages ownership and holds itself to high standards. We invest in people who are curious, thoughtful and adaptable, whether they are launching new products, reporting on global events or harnessing emerging technologies such as AI to improve how we work. Here, fresh thinking is taken seriously, ambition is matched by integrity, and great work is recognised. Working across disciplines, geographies and perspectives, we are united by a commitment to innovation, excellence and creating meaningful impact.
Role Overview
We're looking for an AIO Manager to help evolve our search strategy for the AI\-driven discovery era. This role will report to our Head of Optimization to help optimise content for AI\-powered interfaces such as ChatGPT, Gemini, Perplexity, and other generative engines.
You'll play a key role in ensuring our content is discoverable, interpretable, and cited across both search and AI ecosystems. You'll create long\-term strategies that leverage AI deliberately to drive organic subscriber acquisition growth.
Key Responsibilities
AI Search and Content Optimizations:
- Create optimization strategies that ensure our brand and content is discoverable and cited in AI\-driven environments
- Collaborate with editorial and product teams to ensure all channels are optimized for AI search discoverability
- Build a test\-and\-learn framework to scale successful AI\-driven initiatives across the organisation
- Test various content formats and back\-end engineering strategies to improve visibility in AI\-generated responses
Monitoring and Evaluation:
- Analyze search patterns across LLMs to identify trends and influence content and product roadmaps
- Translate emerging AI search trends into actionable growth opportunities
- Run AIO audits and provide actionable recommendations
- Monitor performance metrics and provide regular reporting on AI referral channels
- Conduct in\-depth keyword and AI prompts research and competitive audits
Cross\-functional Collaboration:
- Partner closely with editorial, product, engineering, and marketing teams to align AI optimization strategies with business goals
- Collaborate with data and analytics teams to define measurement frameworks and reporting for LLMs
- Leverage AI tools and automation to improve marketing performance across acquisition channels and onsite conversion journeys
- Identify opportunities to streamline and automate marketing and reporting workflows, reducing manual effort and increasing speed to execution
- Collaborate with email marketing team on content initiatives such as subject line testing and schedule optimizations
Experience \& Skills
- Strong experience in SEO, search marketing or other technical disciplines
- Experience in AIO / AEO / GEO, AI research and LLMs
- Up to date in current and emerging AI trends
- Experience with analytics tools and SEO softwares
- Strong analytical skills and data driven decision making
- Excellent communication skills and cross\-team collaboration
- Deep understanding of on\-page, off\-page and technical SEO best practices
The expected base salary for this position is $79,000 \- $106,000 base plus a performance related bonus. It is not typical for offers to be made at or near the top of the range. Rather, salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, licensure or certifications obtained. Market and organizational factors are also considered.
\#LI\-Hybrid
Working Arrangements
The majority of our roles operate on a hybrid working pattern, with 3\+ days office attendance required.
AI usage for your application
We are an innovative organisation that encourages the use of technology. We recognise that candidates may utilise AI tools to support with their job application process. However, it is essential that all information you provide truthfully and accurately reflects your own experience, skills, and qualifications.
What we offer
Our benefits package is designed to support your wellbeing, growth, and work\-life balance. It includes a highly competitive pension or 401(k) plan, private health insurance, and 24/7 access to counselling and wellbeing resources through our Employee Assistance Program.
We also offer a range of lifestyle benefits, including our *Work From Anywhere* program, which allows you to work from any location where you have the legal right to do so for up to 25 days per year. In addition, we provide generous annual and parental leave, as well as dedicated days off for volunteering and even for moving home.
You will also be given free access to all *The Economist* content, including an online subscription, our range of apps, podcasts and more.
Salary Context
This $79K-$106K range is in the lower quartile 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 Economist Group, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($92K) sits 57% below the category median. Disclosed range: $79K to $106K.
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.
Economist Group AI Hiring
Economist Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $106K - $106K.
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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