Interested in this AI/ML Engineer role at VIBE?
Apply Now →About This Role
Streaming TV is no longer just a brand awareness channel. It’s becoming a core part of the performance marketing mix, alongside search and social. Vibe.co is building the platform that makes TV work like the rest of the acquisition stack: targeted, measurable, and optimized for results.
Vibe.co is an Audience First Streaming TV Advertising platform that gives marketers the tools to reach the right audiences, optimize campaigns in real time, and understand what’s actually driving business outcomes. With hyper-targeted segmentation, AI-powered recommendations, real-time optimization, and incrementality measurement, Vibe brings the precision and accountability of digital advertising to the biggest screen in the house.
Today, more than 12,000 brands use Vibe.co to reach 120 million+ households. And in August 2026, Vibe.co became a Walmart company, opening up a new chapter for the business and the industry. With Walmart’s first-party data and purchase signals now part of the Vibe platform, advertisers can connect TV advertising to real-world transactions and measure the impact of CTV with a level of precision previously associated with platforms like Google and Meta.
This is a unique moment to join Vibe. With Walmart’s scale, data, and reach behind us, we’re building the next generation of TV advertising and creating a more measurable, performance-driven future for the channel.
About the Role
Data isn't a support function at Vibe, it's the fuel. Every decision about which ad reaches which household, and when, runs on it. You'll join the Data Platform team and specialize in identity, turning billions of raw events into coherent, usable entities that power targeting, measurement, and the training sets Vibe's models learn from. You'll report to the Head of Data Platform. This role exists because identity resolution at Vibe's scale is a hard, unsolved problem, and the team needs someone who can build models that catch the right signal across petabytes of events. You'll get direct, measurable impact: your work decides which ad reaches whom and whether it worked, not a metric buried in a research paper. You'll shape the roadmap for a system every team at Vibe depends on, and work on a problem that only gets more interesting as CTV scales.
What You'll Do
Identity Stack Ownership
- Research and build algorithms that resolve coherent entities, like users and households, from billions of events
- Turn machine learning, clustering, and statistical approaches into scalable, production-ready systems
- Lead projects end-to-end, from stakeholder requirements through research, implementation, and post-launch iteration
- Build platform-level identity-resolution tooling so every team can resolve identity behind their own events
- Design for scale from the start; treat unscalable local tests as unfinished work
Cross-Team Partnership
- Help shape and execute the roadmap for Vibe's identity stack
- Surface tradeoffs, risks, and decisions to stakeholders without burying them in implementation detail
- Translate ambiguous requests from Product, Sales, ML, and Finance into concrete deliverables
- Collaborate with other ML engineers on MLOps problems
What You Need
- 5+ years of experience as an ML engineer or data scientist building and operating production services
- Experience operating at data scale, using tools like Dagster, Spark, or Iceberg
- Familiarity with MLOps tooling and practices
- A track record of leading technical projects end-to-end, from ambiguous requirements to shipped systems
- Strong cross-functional communication skills, including adjusting detail level for your audience and pushing back constructively
- Judgment about when to invest in abstraction and when to keep things simple
Nice to Haves
- Experience in streaming TV advertising or programmatic advertising
Our Values
At Vibe, we run on three values: Impact, Ambition, and Urgency. We tie every goal to a real business outcome, think 10x rather than settling for good enough, and move daily rather than waiting for perfect conditions.
You'll thrive here if you own outcomes without needing direction, default to action, and hold yourself to results, not effort.
This is a high-performance environment with high-performance rewards: you'll work alongside people who push you, with real ownership over hard problems in a market that's moving fast.
If that sounds like the best version of your career, we want to meet you.
What We Offer
The pay range for this role is €99,000 - €171,000. We will support relocation to Paris for great candidates.
At Vibe, we want you to build something, own something, and grow fast. Here's how we back that up:
- Equity — Employee Stock Ownership Plan. You're building this; you should own part of it.
- Variable pay — Based on real Vibe business goals.
- Hybrid flexibility — We're in the Heart of Paris and our team is in 3x a week!
- Health insurance — Full coverage via Alan.
- Meal vouchers — Via Swile.
- Annual offsite — The whole team, once a year, somewhere worth the trip.
- Tech Syncs — Engineering and Product meet in person at least quarterly, worldwide.
Our Interview Process
We respect your time. Here's exactly what to expect — no surprises, no ghosting.
- Recruiter screen — 30 min. We'll share context; you'll share yours.
- Manager interview — Meet the person you'd work with directly.
- Technical Interview — A real problem, not a trick. We'll work through it together.
- Calibration interview — A senior leader joins to ensure we're holding a consistent, high bar.
- Offer — Fast. We don't let good decisions sit.
- Reference checks — Two calls, handled with discretion.
Equal Opportunity
Vibe is an equal opportunity employer. We evaluate all applicants without regard to race, color, religion, national origin, gender, gender identity or expression, sexual orientation, age, disability, veteran status, or any other characteristic protected by law.
We believe the best ideas come from teams with different backgrounds, perspectives, and experiences. If you need a reasonable accommodation during the application or interview process, please let us know.
Referral Instructions
Being referred? Ask your contact to submit your application directly on your behalf.
Salary Context
This $99K-$171K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At VIBE, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $99K to $171K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
VIBE AI Hiring
VIBE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $171K - $171K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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