Senior Software Enginering - Mk8S/Control Plane

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.

We are seeking a Senior Software Engineer to join our Managed Kubernetes (Mk8s) team. You will play a crucial role in shaping the architecture, reliability, and automation of our Kubernetes-based infrastructure, which powers mission-critical workloads across our global platform.

As a Senior Engineer on our Orchestration team, you will contribute to Lambda's managed orchestration services, including Managed Kubernetes, Managed Slurm on Kubernetes, and higher-level platform services for inference and AIOps.

Responsibilities:

• Design, build, and maintain scalable control plane services, operators, and custom Kubernetes controllers
• Develop automation in Go/Python for end-to-end cluster lifecycle management , provisioning, upgrades, patching, and deletion
• Build GPU-aware orchestration systems, working within the platform architecture to support GPU scheduling and resource allocation
• Partner with the Network team on networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect
• Write resilient systems that handle failure gracefully , timeouts, retries, backoff, and degraded-mode operation , across large-scale distributed environments
• Develop platform services for inference: model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns
• Build internal tools and CLIs that let ML/AI teams deploy and monitor their own inference services
• Support and debug production issues through on-call rotation

Requirements:

• 6+ years of experience in software engineering, with a track record of owning significant technical scope within a team
• Deep understanding of Kubernetes internals: controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful
• Solid grasp of distributed systems fundamentals , fault tolerance, graceful degradation, and failure handling in large-scale environments
• Experience operating the control plane and low-level pieces of large-scale Kubernetes clusters
• Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems
• Strong programming skills in Go and Python; ability to collaborate effectively on shared codebases
• Solid knowledge of Linux systems, networking, containers, and cloud infrastructure

Nice to Have:

• Experience building and operating managed Kubernetes services (GKE, EKS, AKS, or similar) or working on Kubernetes control plane components
• Hands-on experience with NVIDIA's GPU/networking ecosystem: GPU Operator, device plugins, DCGM, MIG, Network Operator, NCCL tuning, or similar
• Familiarity with HPC and traditional job schedulers (Slurm) and Kubernetes-native batch scheduling (KAI, Volcano, Kueue)
• Familiarity with GPU, InfiniBand, RDMA, or high-performance computing on Kubernetes
• Exposure to storage architecture for AI/ML workloads
• Past contributions to CNCF projects or Kubernetes SIGs a plus