remote
Machine Learning Systems Engineer, Ads ML Platform
ML Engineer
Design, build, and maintain scalable ML pipelines for an advertising platform using Airflow, Flink, Spark, BigQuery, and Kafka, ensuring high performance and reliability.
About the role
Key Responsibilities
- Architect and develop end‑to‑end ML data pipelines that ingest, process, and serve large volumes of advertising data.
- Leverage Apache Airflow for workflow orchestration, ensuring robust scheduling and monitoring.
- Implement real‑time and batch processing using Apache Flink and Apache Spark to support model training and inference.
- Integrate with Google BigQuery for data warehousing and analytics, optimizing query performance.
- Consume and produce streaming data via Kafka, ensuring low latency and fault tolerance.
- Collaborate with data scientists to translate model requirements into production‑ready pipelines.
Requirements
- Strong experience with Airflow, Flink, Spark, BigQuery, and Kafka in a production environment.
- Proficiency in Python and SQL for data manipulation and pipeline development.
- Solid understanding of ML lifecycle, model deployment, and performance monitoring.
- Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
- Excellent problem‑solving skills and ability to work cross‑functionally in a fast‑paced team.
Skills
apache flinkapache sparkkafkamachine learningpython