Machine Learning Systems Engineer, Ads ML Platform

We’re building a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. Our small but growing team works on projects like batch & realtime feature management platform, training set generation platform, sequence features platform and, agentic and automated ML workflows for feature lifecycle management.

We are looking for an engineer with experience in building high-scale data infrastructure and exposure to ML platforms to help evolve and scale our feature management systems.

This is not a pure ML modeling role. The ideal candidate is excited about building reliable infrastructure, data pipelines, and developer-facing tools that make ML engineers more productive.

• →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.

• 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