About The Role

Join our Advertising Performance team as a Senior Machine Learning Engineer (DevOps/SRE). In this critical role, you will support and scale our Machine Learning infrastructure, streamline the end-to-end ML lifecycle, and lead the design and operation of scalable cloud infrastructure for ML workloads. You will also improve CI/CD systems, define observability standards, and champion operational excellence across ML infrastructure. This position offers a range of benefits, including medical, wellness, and financial benefits, unlimited paid time off, and work from home opportunities.

Lead the design and operation of scalable, production-grade cloud infrastructure for ML workloads across AWS and GCP.
Architect and improve CI/CD systems for ML models and platform services to enable fast, reliable, and safe production releases.
Define and enforce observability standards for ML systems, including model performance monitoring, drift detection, capacity planning, and pipeline health metrics.
Expertise with NoSQL or low-latency data stores such as Aerospike or similar technologies
Strong programming skills in Python and/or Scala or Java for platform automation and tooling
Deep experience with Kubernetes and container orchestration on GCP (GKE) and/or AWS (EKS)
Hands-on experience with data and orchestration technologies such as Apache Spark, Apache Flink, Apache Airflow, and Kafka
Experience building and maintaining CI/CD systems using tools such as Jenkins or GitLab Runner
Familiarity with feature engineering platforms such as Chronon and model lifecycle tools such as MLflow
Excellent communication and cross-functional collaboration skills
Strong infrastructure-as-code experience with Terraform or similar tooling
Experience with observability platforms such as Prometheus, Grafana, and Datadog
8+ years of experience in DevOps, SRE, or ML infrastructure, including 4+ years supporting large-scale ML or AI systems
Experience in the Advertising domain is a plus
BS or MS in Computer Science, Engineering, or a related quantitative field

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