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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer (DevOps/SRE) - **Company:** Roku, Inc. - **Location:** San Jose, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $148,800.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Computer Programming, Continuous Integration, Data Stores, DevOps, Disaster Recovery, Distributed Systems, Python (Programming Language), Machine Learning, NoSQL, Prometheus, Reinforcement Learning, Datadog, Aerospike, Feature Engineering, Grafana, Apache Spark, Gitlab, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Apache Flink, Apache Kafka, Machine Learning Operations, Terraform, Jenkins - **Published:** July 14, 2026 - **Apply:** https://www.weareroku.com/jobs/7931566?gh_jid=7931566 ## About the Role Communication skills Grafana Java GitLab Kubernetes, * The ideal candidate has a strong background in DevOps/SRE practices, cloud infrastructure management, and MLOps tooling - with a passion for building platforms that accelerate ML experimentation and deployment at internet scale * Experience in the Advertising domain is a plus * Hands-on experience with data and orchestration technologies such as Apache Spark, Apache Flink, Apache Airflow, and Kafka * Experience with observability platforms such as Prometheus, Grafana, and Datadog * Expertise with NoSQL or low-latency data stores such as Aerospike or similar technologies * Excellent communication and cross-functional collaboration skills * Deep experience with Kubernetes and container orchestration on GCP (GKE) and/or AWS (EKS) * BS or MS in Computer Science, Engineering, or a related quantitative field * 8+ years of experience in DevOps, SRE, or ML infrastructure, including 4+ years supporting large-scale ML or AI systems * Strong programming skills in Python, and/or Scala, or Java for platform automation and tooling * 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 * Strong infrastructure-as-code experience with Terraform or similar tooling ## Description * The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku * The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency * We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems * At the core of this is our Machine Learning, Experimentation, and Inference Platform that powers the entire landscape, which we continuously evolve over time * We are seeking a talented and experienced Senior Software Engineer, MLOps/DevOps, to join the Advertising Performance team and play a critical role in supporting and scaling our Machine Learning infrastructure * You will partner closely with ML Scientists and Engineers to streamline the end-to-end ML lifecycle across training, evaluation, deployment, and monitoring - on top of a modern, cloud-native stack running on GCP and AWS using Kubernetes, Apache Airflow, Spark, Ray, MLflow, Chronon, etc * Lead the design and operation of scalable, production-grade cloud infrastructure for ML workloads across AWS and GCP, including GPU/TPU-based training and inference environments * Architect and improve CI/CD systems for ML models and platform services to enable fast, reliable, and safe production releases * Own and evolve low-latency infrastructure for real-time model inference, including KV store and vector databases * Define and enforce observability standards for ML systems, including model performance monitoring, drift detection, capacity planning, and pipeline health metrics * Participate in on-call rotation, leading incident response and root-cause analysis for critical ML training and serving infrastructure * Partner with data scientists and ML engineers to improve platform usability, accelerate model iteration, and implement strong MLOps and SRE best practices * Champion operational excellence across ML infrastructure through automation, resilience engineering, disaster recovery planning, and continuous improvement ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [WeAreDevelopers LIVE - Modern DevOps for IoT Devices and More](https://www.wearedevelopers.com/videos/1805-wearedevelopers-live-modern-devops-for-iot-devices-and-more) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [NoSQL Data Modeling for Front-end Developers](https://www.wearedevelopers.com/videos/297-nosql-data-modeling-for-front-end-developers) ## Related Articles - 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