Identity & Audience ML Engineer — Hybrid (Paris)

Vibe; Paris; Hybrid; EUR 99,000 - 171,000; Full time

Benefits offered by this job

Relocation to Paris

Equity — Employee Stock Ownership Plan

Health insurance

Meal vouchers

Annual offsite

Hybrid work — 3 days in Paris office

Job summary

Vibe seeks an experienced ML engineer to lead identity resolution at scale within its Data Platform team in Paris. You will own the identity stack, research and implement algorithms to resolve users and households from billions of events, and turn ML approaches into production systems.

You’ll work cross-functionally with Product, Sales, and Finance to translate ambiguous requests into concrete deliverables, and collaborate with other ML engineers on MLOps challenges.

Qualifications

• 5+ years of experience as an ML engineer or data scientist building and operating production services.
• Experience operating at data scale with tools like Dagster, Spark, or Iceberg.
• Familiarity with MLOps tooling and practices.
• Track record of leading technical projects end-to-end, from ambiguous requirements to shipped systems.
• Strong cross-functional communication and ability to tailor detail level for the audience.

Responsibilities

• Own Identity Stack and lead end-to-end development.
• Research and build scalable identity resolution algorithms for users and households.
• Translate ML research into production services and tooling.
• Collaborate with ML engineers on MLOps problems and cross-team initiatives.
• Shape the roadmap for Vibe's identity stack and manage stakeholder expectations.

Skills

ML engineering

Production systems

MLOps

Cross-functional communication

Data scale

Tools

Dagster

Spark

Iceberg

Job description

Vibe seeks an experienced ML engineer to lead identity resolution at scale within its Data Platform team in Paris. You will own the identity stack, research and implement algorithms to resolve users and households from billions of events, and turn ML approaches into production systems.

You’ll work cross-functionally with Product, Sales, and Finance to translate ambiguous requests into concrete deliverables, and collaborate with other ML engineers on MLOps challenges.