Site Reliability Engineer, Criteo

• Strong experience with Kubernetes, especially in dynamic provisioning and custom operators
• Hands-on experience with GPU workloads, ideally in ML training or inference contexts
• Solid programming skills in C#, Python, Go, or similar languages
• Passion for automation, observability, and building reliable services

Desirable

• At Criteo, our Platform Core teams build the foundational services that power our global advertising platform
• We design and operate scalable, resilient systems that support real-time decision-making and data processing at massive scale
• As we expand our capabilities in high-performance inference and distributed computing, we’re forming a new team focused on GPU-powered services and cutting-edge ML serving technologies
• As a Site Reliability Engineer in this new team, you’ll be at the forefront of building and operating GPU-powered services for machine learning workloads
• Your mission will be to ensure the reliability, scalability, and performance of our systems that leverage:
• Ray: You’ll manage on-demand provisioning of Ray clusters on Kubernetes, enabling scalable distributed computing as a service for ML training and inference. You’ll design, maintain, and monitor these ray-as-a-service systems, and deliver these capabilities as robust, self-service platform offerings
• Nvidia Triton Inference Server: You’ll optimize and operate high-performance inference services using Triton, ensuring low-latency and high-throughput serving of deep learning models
• You’ll work closely with ML engineers, data scientists, and other infrastructure teams to deliver production-grade services that accelerate innovation across Criteo