Senior Machine Learning Engineer, DevOps/SRE

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, which powers the entire landscape, and we continuously evolve.

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. 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.

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

- BS or MS in Computer Science, Engineering, or a related quantitative field

- Ability to demonstrate AI tool (Claude, Cursor, ChatGPT etc.) coding efficiency at an advanced skill level

- 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

- Deep experience with Kubernetes and container orchestration on GCP (GKE) and/or AWS (EKS)

- Expertise with NoSQL or low-latency data stores such as Aerospike or similar technologies

- 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

- Strong infrastructure-as-code experience with Terraform or similar tooling

- Experience with observability platforms such as Prometheus, Grafana, and Datadog

- Excellent communication and cross-functional collaboration skills