Staff Engineer

We are seeking a Staff AI Orchestration Engineer to lead the design, optimization, and scaling of our Kubernetes-based AI infrastructure. In this role, you will tackle the unique challenges of massive-scale AI workloads, focusing on throughput, GPU utilization, and fault tolerance to support next-generation distributed training and disaggregated inference.

• Architect Large-Scale Scheduling: Design and optimize hierarchical, high-throughput scheduling architectures for massive Kubernetes clusters (1,000+ nodes, 10,000+ pods), utilizing techniques like optimistic concurrency, multi-scheduler architectures, and batch dispatching.
• Maximize GPU Utilization: Eliminate GPU waste in multi-tenant environments by implementing fractional GPU allocation, leveraging mechanisms like KAI-Scheduler's Reservation Pods or hard-isolation tools like HAMi, and configuring time-based fairshare scheduling to balance over-quota pool access.
• Optimize Placement & Topology: Deploy topology-aware scheduling to align pod placement with physical hardware dimensions, such as NVLink connections, PCIe lanes, and NUMA nodes, minimizing communication latency for multi-GPU operations.
• Enhance Cluster Performance: Reduce scheduling latency and API server load by tuning etcd, optimizing admission webhooks, and implementing in-place pod resizing (VPA) or in-place container restarts.
• Secure AI Workloads: Design secure, multi-layered isolation environments and Agent Sandboxes to safely execute untrusted LLM-generated code, utilizing namespaces, Kata Containers, gVisor, or Firecracker microVMs.
• Manage AI Storage & Fault Tolerance: Orchestrate efficient model weight distribution using OCI Image Volumes and implement Checkpoint/Restore capabilities (via CRIU and NVIDIA cuda-checkpoint) for long-running training fault recovery.
• Enable Distributed Training: Implement robust gang scheduling to prevent deadlocks in tightly-coupled, multi-node training jobs (e.g., MPI, PyTorch) using tools like Volcano, Kueue, or LeaderWorkerSet (LWS).
• Orchestrate Complex Inference: Implement and manage disaggregated AI inference pipelines using frameworks like NVIDIA Grove, coordinating multicomponent deployments (e.g., prefill leaders, decode workers, KV routers) with multilevel autoscaling and explicit startup ordering.

• Kubernetes Expertise: Deep technical knowledge of Kubernetes core components, API performance optimization, Dynamic Resource Allocation (DRA), and the custom resource definitions (CRDs) required for advanced scheduling.
• Advanced Scheduling Experience: Proven track record working with AI-specific Kubernetes schedulers and orchestrators such as Kueue, Volcano, Apache YuniKorn, or Run:ai / KAI-Scheduler.
• Hardware & Topology Acumen: Deep understanding of GPU architectures (NVIDIA and AMD) and interconnects, understanding how hardware topology directly impacts training and inference speeds.
• Resource Management Skills: Experience balancing performance and cost using Dominant Resource Fairness (DRF), load-aware scheduling, and bin-packing vs. spread strategies to maximize node vacancy or workload resources.
• Systems Isolation Background: Familiarity with container runtime internals (containerd, runc), rootless containers, and security contexts to manage blast radiuses in shared AI infrastructure.
• AI/ML Framework Knowledge: Strong understanding of modern LLM serving architectures, prefill-decode disaggregation, and engines like vLLM, Triton, or SGLang.
• Observability Proficiency: Experience tracking deep infrastructure and inference metrics, including Time To First Token (TTFT), Time Per Output Token (TPOT), GPU memory pressure, and identifying hardware failures like XID errors.

• $191,200 - $239,000

*This is a hybrid role

JR: 2026-7729

#LI-Hybrid