Remote ML Engineer: End-to-End Models & Platform

Alma

France

Vor Ort

EUR 90.000 - 120.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Benefits dieser Stelle

Health insurance: 100% covered by Alma
Disability insurance: 100% covered by
Sport partnerships with Gymlib/Classpp
Maternity/paternity leave: 100% paid
Sustainable Mobility Package (€544.80/

Zusammenfassung

Alma, the BNPL leader in France, is hiring a Machine Learning Engineer to own ML products end-to-end, splitting time between model development and the platform that runs them. The role is Paris-based or fully remote within France.

You will manage models through data pipelines, training, deployment and monitoring; own the ML platform CI/CD, observability, and SLOs; and partner with risk, operations, and product teams to translate ML into business value.

Qualifikationen

  • 5+ years building and shipping ML in production.
  • Strong Python and SQL fundamentals and clean, tested code.
  • Familiar with parts of Alma's stack (GCP, Docker, graph databases).

Aufgaben

  • Own ML models end-to-end: data pipelines, feature engineering, training, deployment and monitoring.
  • Run and improve the ML platform: CI/CD, endpoints, latency, load, and SLOs with observability.
  • Turn ML into business value: collaborate with risk, operations and product teams.

Kenntnisse

Python
SQL
MLOps
Data pipelines
English (professional)
French (working)

Tools

Vertex AI Pipelines
Kubeflow
dbt
BigQuery
PostgreSQL
Argo Workflows
Neo4j
FastAPI
Argo CD
Kubernetes
Terraform
Datadog

Jobbeschreibung

Alma, the BNPL leader in France, is hiring a Machine Learning Engineer to own ML products end-to-end, splitting time between model development and the platform that runs them. The role is Paris-based or fully remote within France.

You will manage models through data pipelines, training, deployment and monitoring; own the ML platform CI/CD, observability, and SLOs; and partner with risk, operations, and product teams to translate ML into business value.

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