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Lambda is building AI cloud infrastructure and seeks a Senior Software Engineer to join the Managed Kubernetes (Mk8s) team. You will shape the architecture, reliability, and automation of Lambda's Kubernetes-based infrastructure powering mission-critical AI workloads globally. The role sits at the intersection of distributed systems, GPU-accelerated computing, and cloud-native infrastructure.
You will design, build, and maintain scalable control plane services, operators, and custom Kubernetes controllers. You'll develop automation in Go/Python for end-to-end cluster lifecycle management including provisioning, upgrades, patching, and deletion. You'll build GPU-aware orchestration systems supporting GPU scheduling and resource allocation within the platform architecture.
Key responsibilities include partnering with the Network team on networking solutions for AI workloads (CNI integration with Cilium/Multus, high-performance fabrics like InfiniBand/RoCE, RDMA, GPUDirect), writing resilient systems that handle failure gracefully across large-scale distributed environments, and developing platform services for inference including model serving infrastructure and autoscaling. You'll also build internal tools and CLIs enabling ML/AI teams to deploy and monitor inference services, and participate in on-call rotation for production support.
This is not an operational Kubernetes role—it requires deep understanding of how compute, network, storage, and security interact. You'll work closely with NVIDIA's open-source ecosystem and partner across internal teams to deliver a world-class managed platform serving tens of thousands of customers ranging from AI researchers to enterprises and hyperscalers.
Lambda was founded in 2012, has 500+ employees, and is backed by investors including NVIDIA, Andrej Karpathy, ARK Invest, In-Q-Tel, and others. The company has published research at NeurIPS, ICCV, SIGGRAPH, and TOG.
REQUIREMENTS:
- 6+ years of software engineering experience with a track record of owning significant technical scope (e.g., driving projects from design through production or acting as de facto tech lead)
- Deep understanding of Kubernetes internals: controllers, schedulers, operators, CRDs, CSI, CNI, and extension patterns
- Solid grasp of distributed systems fundamentals—fault tolerance, graceful degradation, failure handling in large-scale environments
- Experience operating control plane and low-level pieces of large-scale Kubernetes clusters
- Experience with observability at scale: Prometheus, Grafana, distributed tracing, actionable alerting systems
- Strong programming skills in Go and Python with ability to collaborate on shared codebases
- Solid knowledge of Linux systems, networking, containers, and cloud infrastructure
NICE TO HAVE:
- Experience building/operating managed Kubernetes services (GKE, EKS, AKS) or working on Kubernetes control plane components
- Hands-on experience with NVIDIA GPU/networking ecosystem: GPU Operator, device plugins, DCGM, MIG, Network Operator, NCCL tuning
- 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
