Staff Software Engineer - Managed Kubernetes

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.