Machine Learning Systems Engineer, Ads ML Platform

What You’ll Do

• 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.

What You Bring

• 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