Machine Learning Systems Engineer, Ads ML Platform

Reddit, Inc.

Machine Learning Systems Engineer developing data infrastructure for Reddit's Ads ML Platform. Collaborating with ML engineers to enhance feature management systems and workflows.

Posted 6/23/2026full-timeRemote • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowBigQueryKafkaKubernetesPySparkRaySpark

About the role

Key responsibilities & impact

Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.
Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.
Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning
Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.
Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management
Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.

Requirements

What you’ll need

3+ years in data infrastructure/platform engineering or ML infrastructure platforms.
Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.
Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.
Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.
Strong coding skills and ability to write clean, maintainable, well-tested code.
Experience building intelligent automation or agentic workflows for ML systems is a strong plus
Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus

Benefits

Comp & perks

Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
Family Planning Support
Gender-Affirming Care
Mental Health & Coaching Benefits
Group Personal Pension Scheme with Employer match
Private Medical and Dental Scheme
Income Replacement Programs
Bike to Work scheme
Flexible Vacation & Paid Volunteer Time Off
Generous Paid Parental Leave

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data infrastructurefeature engineeringdata pipelinesAPIsworkflow systemsdistributed data systemsSparkPySparkKafkaMLOps

Soft Skills

reliabilityscalabilityclean codemaintainable codewell-tested codeoperational excellenceperformance tuningcost optimization