Staff Software Engineer - Managed Kubernetes

Staff Engineer 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. If you'd like to build the world's best AI cloud, join us. *Note: This position requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda's designated work from home day is currently Tuesday. About the Role Lambda is building the AI Cloud of the future. We are seeking a Staff Engineer to help our development of our Managed Kubernetes platform. Think GKE, but purpose-built for AI workloads and running on bare metal. This is a foundational technical leadership role where you will shape the infrastructure that powers the next generation of AI training and inference at scale. As a Staff Engineer on our Orchestration team, you will collaborate to help drive the technical vision for Lambda's managed orchestration services, including Managed Kubernetes, Managed Slurm on Kubernetes, and higher-level platform services for inference and AIOps. You'll work at the intersection of distributed systems, GPU-accelerated computing, and Cloud Native infrastructure to build systems that are reliable, performant, and elegantly simple for our customers. This is not a role for someone who just operates Kubernetes; it is a technical leadership role for an engineer who has synthesized the core domains of infrastructure (compute, network, storage, security) and can design holistic solutions across all of them. You'll be working closely with NVIDIA's open-source ecosystem, and partnering with internal teams across the stack to deliver a world-class managed platform. What You'll Do: Product Engineering Drive technical vision for Lambda's Managed Kubernetes bare-metal platform, including control plane scalability, multi-tenancy, cluster lifecycle management, and high availability Integrate and extend NVIDIA's open-source ecosystem: GPU Operator, Network Operator, DCGM, NCCL, and emerging projects like AICR and Topograph for topology-aware scheduling and placement Design GPU-aware orchestration systems Lead development of services that power our managed services Inform on and help with networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect. You will work closely with our Network team to define and drive requirements Inform and help with storage architecture requirements for AI workloads. You will partner with Storage teams on what managed K8s, Slurm, and future services need Build the foundation for Managed Slurm on Kubernetes, enabling traditional HPC workloads to run seamlessly alongside Kubernetes workload Design higher-level platform services for inference, including model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns Design self-healing systems and automation for incident response, root cause analysis, and platform resilience Lead chaos engineering efforts to validate system behavior under failure conditions at scale Establish operational excellence for a managed service: upgrade automation, security patching, and zero-downtime maintenance Cross-Functional Infrastructure Leadership Serve as the technical bridge between Orchestration and other infrastructure teams (Network, Storage, Security), translating platform requirements into actionable specifications Drive infrastructure-wide decisions that enable successful managed services. You're someone who understands what's needed end-to-end, not just at the Kubernetes layer. Provide input on bare-metal provisioning, network topology, and storage systems to ensure they meet the needs of managed the services being built by the Orchestration organization Champion consistency and standardization across Lambda's infrastructure stack Work directly with customers and internal teams to understand existing deployments and chart a path to the managed platform Technical Leadership Set technical direction for Kubernetes services across the Orchestration team, influencing roadmap and prioritization Drive reviews and design sessions, ensuring we build systems that are scalable, maintainable, and aligned with customer needs Mentor and grow engineers, establishing best practices for Kubernetes development, distributed systems, and Cloud Native engineering Collaborate cross-functionally with Network, Storage, Security, and Customer Success teams Engage with NVIDIA and the open-source community to stay current on GPU orchestration technologies and contribute back where appropriate Represent Lambda externally through technical blog posts, conference talks, and strategic customer engagements Shape our AIOps vision: design intelligent systems for automated capacity planning, anomaly detection, and predictive maintenance of cloud infrastructure Who You Are You are a creative, innovative engineer who operates at high velocity. You don't just solve problems. You find elegant solutions and ship them quickly. You embrace modern tools and AI-assisted development (like Claude Code) to accelerate your productivity and multiply your impact. You're energized by building new things, not maintaining the status quo. Required Qualifications 10+ years of experience in software engineering, platform engineering, or SRE, with at least 5 years focused on Kubernetes at scale Expert-level understanding of Kubernetes internals: API machinery, controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful Holistic infrastructure expertise: you've synthesized knowledge across compute, networking, storage, and security, not just Kubernetes in isolation. You can build solutions that span the full stack. Strong software engineering skills in Go (required) and Python; you write production-quality code, not just scripts Deep experience with GPU orchestration in Kubernetes: NVIDIA GPU Operator, device plugins, DCGM, MIG, time-slicing, and GPU-aware scheduling. Familiarity with NVIDIA Network Operator and GPUDirect is strongly preferred. Proven track record of technical leadership: driving design decisions across teams, mentoring engineers, and influencing infrastructure direction beyond your immediate scope Deep experience designing and operating managed services or multi-tenant platforms. You understand what it takes to run infrastructure for external customers Strong understanding of distributed systems principles: consensus, fault tolerance, consistency models, and graceful degradation Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems Solid knowledge of Linux systems and networking (L2-L7), including high-performance networking concepts (RDMA, InfiniBand, RoCE) Experience with infrastructure-as-code and GitOps workflows Preferred Qualifications 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 open-source ecosystem beyond GPU Operator: Network Operator, NCCL tuning, Topograph, AICR, or similar emerging projects Familiarity with HPC and traditional job schedulers (Slurm) and Kubernetes-native batch scheduling (KAI, Volcano, Kueue) Background in confidential computing Experience migrating customers or workloads from legacy/bespoke infrastructure to standardized platforms Contributions to CNCF projects, Kubernetes SIGs, or NVIDIA open-source projects Familiarity with security and compliance in multi-tenant environments: RBAC, Pod Security Standards, network policies, workload isolation Background in ML infrastructure: training clusters, inference serving, simulation Why Lambda Lambda is building the essential infrastructure for the AI era. We're not just another cloud provider: we're a company founded by ML practitioners, for ML practitioners. Our customers include leading AI research labs and enterprises pushing the boundaries of what's possible with artificial intelligence. What makes this role special: You'll be building core platform services the world's largest AI companies will consume NVIDIA partnership: Deep integration with NVIDIA's GPU and networking stack, working with cutting-edge open-source tooling Real technical challenges: Massive scale GPU clusters and the unique demands of AI workloads Cross-stack influence: Shape not just Kubernetes, but the Lambda