Staff Software Engineer - Managed Kubernetes

Lambda•Seattle, WA

7d•$314,000 - $465,000•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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