AdTechTalent
Engineering10 days agoRemote

Vibe.co

Senior SWE / ML Engineer - Identity & Audience

machine learningml engineeridentity resolutiondata pipelinessparkdagstericebergstreaming tv advertisingprogrammaticproduction systemsclustering algorithm

Key details

Salary

Not specified

Employment type

Full-time

Seniority

Senior

Years experience

5-10

Location

Paris, France

Full job description

Senior Machine Learning Engineer role focused on identity resolution at scale for Vibe.co's streaming TV advertising platform. Responsibilities include designing and operating data pipelines, improving clustering algorithms for user and household identity, anticipating scaling challenges, managing end-to-end projects, building identity-resolution tooling, and shaping the identity stack roadmap. Requires 5+ years experience in ML engineering with production systems, expertise in orchestrators like Dagster, distributed compute like Spark, and large-scale storage like Iceberg. Strong communication skills and ability to manage performance and cost at scale are essential. Hybrid work model based in Paris, with benefits including variable pay, health insurance, meal vouchers, annual offsite, and quarterly tech syncs.

What you'll do

  • Design and operate pipelines for coherent user views across data sources
  • Own and improve clustering algorithm for user and household entity resolution
  • Anticipate scaling issues before incidents
  • Run projects end-to-end from requirements to post-launch iteration
  • Build platform-level identity-resolution tooling for teams
  • Choose scalable and cost-effective architectures
  • Shape and execute the roadmap for Vibe's identity stack
  • Communicate tradeoffs, risks, and decisions to stakeholders
  • Translate ambiguous requests from Product, Sales, ML, and Finance into concrete deliverables

Requirements

  • 5+ years of experience as an ML engineer building and operating production services
  • Hands-on experience with a modern orchestrator (e.g. Dagster), distributed compute (e.g. Spark), and large-scale storage (e.g. Iceberg)
  • Track record of managing performance and cost at scale with genuinely large datasets
  • Ability to adapt technical communication to different audiences and align stakeholders with conflicting priorities
  • Bias toward shipping value, with judgment about when to build abstractions and when to keep things simple
  • Experience running systems in production, not just prototyping them

Tech stack

machine learningDagsterSparkIcebergidentity resolutiondata pipelinesclustering algorithms

Benefits

Variable pay based on objectivesHybrid flexibility (3x a week in Paris office)Full health insurance coverage via AlanMeal vouchers via SwileAnnual offsite for the whole teamQuarterly in-person Tech Syncs with Engineering and Product teams worldwide

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