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About This Role
Hey there!
We’re Fever, the world’s leading tech platform for culture and live entertainment.
Our mission? To democratize access to culture and entertainment. With our proprietary cutting\-edge technology and data\-driven approach, we’re revolutionizing the way people engage with live entertainment.
Every month, our platform inspires over 300 million people in \+55 countries (and counting) to discover unforgettable experiences while also empowering event creators with our data and technology, helping them scale, innovate, and enhance their events to reach new audiences.
Our results? We’ve teamed up with major industry leaders like Netflix, F.C. Barcelona, and Primavera Sound, presented international award\-winning experiences, and are backed by several leading global investors! Impressive, right?
To achieve our mission, we are looking for bar\-raisers with a hands\-on mindset who are eager to help shape the future of entertainment!
Ready to be part of the experience?
Now, let’s discuss this role and what you will do to help achieve Fever’s mission.
Meet The Team
Our SEO team ensures our platforms, from SMN and Marketplace to landings and Local Business Cards, are seen by the right audience. We combine data, creativity, and technical expertise to boost visibility, drive organic traffic, and optimize every user experience. Working with Product and Content teams, we build features and strategies that keep us ahead in search.
Join us and help shape how millions discover our products every day.
The Role
At Fever, we’re building something exceptional—combining strategy, creativity, and the power of advanced technology to redefine what’s possible in organic search. By partnering with world\-class Product, Engineering, Data and Marketing teams, we leverage cutting\-edge tools and data\-driven insights to deliver exceptional performance and measurable impact across major search engines and dynamic digital ecosystems.
As an AI Search Innovation Strategist, you will drive the evolution of our brand's authority in the next era of search, focusing entirely on impact over traditional acronyms. You will lead the testing and measurement of how LLMs construct answers, designing the analytical frameworks to track our visibility across major AI models and emerging platforms like MCPs. Collaborating closely with Product, Engineering, and PR teams, you will optimize our architecture for seamless AI ingestion and execute strategies to actively shape and accelerate LLM perception.
What You´ll Do
Forget about acronyms like SEO, GEO, AEO or AEO, we care about impact. We're seeking an AI Search Innovation Strategist obsessed with digging into how LLMs build their answers and defining how to measure it. You'll be focused on testing new tools, MCPs, and platforms, evolving SEO discipline to maximize our brand's authority in the next era.
- Own the strategic evolution of core SEO practices by integrating methods to secure our brand’s authoritative presence across all major LLMs and AI search results.
- Lead testing and measurement: Design and execute testing to decipher how established SEO signals influence LLM citations and generative presence.
- Design and maintain the analytical framework to measure AI visibility performance. Proactively integrate new data sources, external and internal, and provide methodologies to ensure accurate reporting.
- Collaborate intensively with Product and Engineering to ensure our data schema, site architecture, and API feeds are optimally engineered for seamless ingestion and prioritization by AI models.
- Continuously evaluate all new LLM releases, AI models, and emerging platforms (including MCPs), translating industry developments into actionable testing strategies.
- Design and execute PR and community conversation strategies to accelerate and measure LLM perception.
Who You Are
- You hold a Bachelor’s or Master’s degree in Engineering, Mathematics, Statistics, Data Analytics or Big Data (STEM degree)
- You bring to the table 3\-5 years of relevant experience on SEO, demonstrating ownership, attention to detail, and a proactive, self\-motivated approach to problem solving
- Strong analytical and quantitative skills as well as a strong bias towards data\-based decision
- Your exceptional written and verbal communication skills enable you to excel in cross\-functional collaboration and effectively convey complex concepts to diverse audiences with varying backgrounds
- Working knowledge in Python, JavaScript, or other programming languages is required and familiarity with SQL is a plus
- Proficiency in both written and spoken English is a must, while knowledge of additional languages is a valuable asset.
Why You´ll Love It Here
- Attractive compensation package with room to grow.
- 40% discount on all Fever events and experiences \+ free Candlelight voucher.
- Flexible remuneration with Cobee: meals, transport, training, and nursery support (tax\-exempt).
- Payflow: option to access part of your salary in advance.
- Taxdown: help optimizing and filing your taxes.
- Private health \& dental insurance with Cigna: 100% covered by Fever. You can also add your family members via flexible remuneration.
- Wellhub membership for gym and wellness access: 100% covered by Fever up to the Silver plan.
- Language learning support: English and Spanish lessons.
- Hybrid work setup: home office friendly with a central Madrid office location.
- Snacks, drinks, and fresh fruit are always available at the office.
- Dynamic international team: a young, talented group creating a great work environment.
\#LI\-Hybrid
Thank you for considering joining Fever. We cannot wait to learn more about you!
If you want to learn more about us: Fever's Blog \| Tech.Eu \|TechCrunch
Fever is committed to creating an inclusive and diverse workspace where everyone's background and ideas count. Our main goal is to find the best possible talent regardless of place of birth, racial or ethnic origin, gender, gender identity, religion, opinion, sexual orientation, disability, pregnancy, marital status, age or caring responsibilities. We encourage everyone to apply!
If you require any kind of accommodation during the selection process please contact our Talent team so we can help you by providing a welcoming and seamless journey.
If you want to know more about how Fever processes your personal data, click here Fever \- Candidate Privacy Notice
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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At FeverUp, 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 $175,000 based on 11,128 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
FeverUp AI Hiring
FeverUp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $202,560 across 264 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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