Machine Learning Systems Engineer, Ads ML Platform
New
R
RedditAds ML Platform
You can work remotely from anywhere in the UK or the Netherlands.Full-TimeMiddle
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 3+ years in data infrastructure/platform engineering or ML infrastructure platforms.
- Required Skills
- KafkaKubernetesAirflowSparkBigQueryMLOps
Requirements
- 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.
Responsibilities
- 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.
View Full Description & ApplyYou'll be redirected to the employer's site
