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# Senior Machine Learning Engineer - Identity & Audience

- **Company:** VIBES LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Infrastructure, Machine Learning, Apache Spark, Machine Learning Operations
- **Published:** September 25, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pmvz1px4f1

## About the Role

* 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

## Description

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


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