Machine Learning Systems Engineer, Ads ML Platform

• Build a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. • Work on projects including batch and real-time feature management, training set generation, sequence features, and agentic and automated ML workflows for feature lifecycle management. • Build reliable infrastructure, data pipelines, and developer-facing tools that make ML engineers more productive — this is not a pure ML modelling role.

• Bring 3+ years of experience in data infrastructure/platform engineering or ML infrastructure platforms. • Have hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools. • Have experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies. • Bring familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving. • Demonstrate strong coding skills and the ability to write clean, maintainable, well-tested code.

• Have experience building intelligent automation or agentic workflows for ML systems. • Bring experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving.

• 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 optimisation initiatives.

• Access global benefit programmes that fit your lifestyle, from workspace to professional development to caregiving support. • Receive family planning support. • Access gender-affirming care. • Use mental health and coaching benefits. • Join the Group Personal Pension Scheme with employer match. • Access private medical and dental scheme. • Benefit from income replacement programmes. • Use the Bike to Work scheme. • Enjoy flexible vacation and paid volunteer time off. • Take generous paid parental leave.

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