Senior Site Reliability Engineer, Criteo

• Master's or PhD in Computer Science (or equivalent experience)
• 5+ years in backend engineering, SRE or DevOps
• 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
• We acknowledge that many candidates may not meet every single role requirement listed above. If your experience looks a little different from our requirements but you believe that you can still bring value to the role, we’d love to see your application!​

Desirable

• 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
• 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