Staff / Senior Staff AI Infra Engineer (Inference Infrastructure)

Join our team as a Staff/Senior Staff AI Infra Engineer, where you'll be responsible for the reliability and efficiency of our GPU inference fleet. You'll work closely with the inference/ML platform team, machine learning researchers, engineers, and product managers to bring our latest technologies into production. Your role will involve triaging issues, extending automated self-healing systems, and owning end-to-end reliability of the pipeline. You'll also have the opportunity to introduce new techniques, tools, and architecture to improve our model inference stack.

• Assumer la responsabilité de la fiabilité de la production pour les systèmes distribués à grande échelle, en particulier l'infrastructure GPU/ML.
• Collaborer avec les équipes de plateforme d'inférence/ML pour intégrer les dernières technologies en production et améliorer les performances.
• Diriger les initiatives de fiabilité à travers les équipes d'infrastructure, de plateforme ML et de produit, en gérant les parties prenantes lors des incidents.

• A demonstrated track record of eliminating operational toil through automation and tooling you built yourself, not just process or documentation
• Deep experience owning production reliability for large-scale distributed systems; GPU/ML-serving infrastructure experience is a strong plus
• Hands-on experience with Kubernetes controller/CRD patterns, reconciliation loops, and node-level health/remediation systems
• Familiarity with the operational realities of multi-tenant model serving (LoRA/adapter-based serving) and with running differentiated SLOs across model pools of different priority
• Comfort with event-driven architectures, backpressure/circuit-breaking design, and treating observability (metrics, logs, traces) as something built into a system, not added after an incident
• Experience operating a provisioning or host-lifecycle pipeline in production, comfortable troubleshooting stuck or failed state transitions (discovery, bring-up, GPU driver/CUDA validation, health checks, decommission/RMA) at fleet scale, and translating real failure patterns back into improvements to the state machine’s design
• A software-engineering mindset first: you write and ship production code, and you think about GPU infrastructure as software to be engineered, not a fleet to be manually operated
• Hands-on experience troubleshooting InfiniBand/RDMA networking issues, ideally in a multi-node training or disaggregated-inference context — comfortable reading IB port counters and subnet manager logs, not just application metrics