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About This Role
About Wurl, LLC.
At Wurl, our mission is to create innovative technologies and services that help accelerate the shift to streaming. At our core, we are innovators focused on driving the streaming industry forward with technologies that help connect viewers to the right content and ads. Our technology and data-driven solutions help companies reach new audiences, engage high-value viewers, and ultimately increase their revenue.
In 2022, Wurl was acquired by AppLovin (Nasdaq: APP), a leading marketing platform, giving our employees the best of both worlds: the dynamic environment of a 150+ person startup as well as the stability of a high-growth public tech company. We invest in providing a culture that fosters passion, drives excellence, and encourages collaboration to drive innovation. Wurl is a fully-remote company that has been globally recognized for our people and technology, having been named a Great Place to Work by Fortune in 2022, 2023, and 2024; an Ad Tech Company of the Year finalist by the UK Business Tech Awards in 2023; a BIG Innovation Awards winner in 2024 and 2025; and the “Innovation in Advertising” winner by the StreamTV Awards in 2024.
About the role:
Wurl is seeking an intern with proven academic achievements in data analytics related fields for a year long internship. Wurl’s position in the video and ad delivery ecosystem generates large quantities of data that needs to be carefully analyzed and normalized. We’re looking for an intern that can become familiar with Wurl’s datasets and their usage and support our Data Science and Machine Learning team. The ideal candidate is a PhD / MSc in the field of DataScience, AI/ML, Statistics, Physics, Mathematics, or Computer Science.
Responsibilities:
- Perform analysis tasks to identify anomalies and their root cause as well as propose and put in place validations to programmatically identify such conditions
- Propose methods to normalize the data, implement them, check accuracy and iterate until sufficient accuracy is achieved
- Increase the accuracy of derived datasets and data products
Qualifications:
- Education -
- + PhD / MSC preferred. BSc with high gpa, multiple majors, notable projects is also accepted + Field: DataScience, AI/ML, Statistics, Physics, Mathematics, or Computer Science
- Technical + Graph algorithms and network analysis + Large-scale data analysis + Data quality and anomaly detection + Agentic / AI-assisted software development + Familiarity with distributed data platforms such as Snowflake / Databricks
- + Strong Python and SQL + Applied statistics and probability + Machine learning fundamentals + Experiment design and causal reasoning + Optimization / decision-making under uncertainty + Ability to write clean, maintainable analytical code + Non agentic development experience is mandatory + Nice to have
- Modeling / Research + Comfortable evaluating models beyond headline metrics
Experience moving from research idea prototype+ validated result
- + Ability to translate ambiguous problems into mathematical or statistical formulations + Strong intuition for uncertainty, bias, variance, and noisy data + Strong quantitative reasoning and mathematical knowledge + Nice to have
- Problem Solving + Ability to recognize when results look wrong + Comfortable working with incomplete, messy, or contradictory data
- + Advanced analytical problem-solving ability + Strong debugging and root-cause analysis skills + Ability to form and test hypotheses quickly + Nice to have
- Personal Attributes
- + High intellectual curiosity + Strong ownership and initiative + Rapid learner + Independent thinker + Comfortable challenging assumptions + Willingness to continue digging into 'funny' things until they make sense + Effective communication skills and team player.
What We Offer:
- Paid internship ($30/hr) with flexible scheduling.
- Remote-first work culture and support.
- Mentorship from experienced engineers and product experts.
AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant here.
If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at [email protected]
AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in California, learn more here.
Please read our Global Applicant Privacy Notice to learn more about how AppLovin processes your personal information.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wurl, 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 $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wurl AI Hiring
Wurl has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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