Ads ML Platform Engineer — Scale Feature Engineering

Reddit, Inc.; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Global benefits

Family Planning Support

Mental Health & Coaching

Pension plan with employer-matching

100% employer-sponsored medical plan

Paid time off

Parental Leave

Job summary

Reddit, Inc. is expanding its data infrastructure to support large-scale feature and training set computation across batch and real-time workflows. You will build reliable pipelines, governance features, and integration points with ML production systems.

The role emphasizes scalable design, clean code, and collaboration with ML engineers to enable end-to-end feature engineering and model training in a remote-friendly environment.

Qualifications

• 3+ years in data infrastructure/platform engineering or ML infra.
• Hands-on experience building production services, data pipelines, APIs, workflow systems, or dev tools.
• Experience with distributed data/compute systems (Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery).
• Familiarity with ML data workflows (feature generation, training datasets, batch/real-time processing).
• Strong coding skills and maintainable, tested code.

Responsibilities

• Design and build data infrastructure for large-scale feature and training set computation, transformation, and storage.
• Develop batch and real-time feature frameworks focusing on reliability and scalability.
• Build governance capabilities for feature lineage, validation, drift detection, and versioning.
• Collaborate with ML engineers to integrate feature engineering into ML production systems.
• Develop systems for agentic ML workflows including automated feature discovery and evaluation.

Skills

Data infrastructure

Distributed data systems

Programming

ML workflows

Observability

Tools

Spark

PySpark

Flink

Kafka

Airflow

Kubernetes

BigQuery

Job description

Reddit, Inc. is expanding its data infrastructure to support large-scale feature and training set computation across batch and real-time workflows. You will build reliable pipelines, governance features, and integration points with ML production systems.

The role emphasizes scalable design, clean code, and collaboration with ML engineers to enable end-to-end feature engineering and model training in a remote-friendly environment.