Staff Software Engineer (Managed Kubernetes)

Join Lambda, a company dedicated to building the AI Cloud of the future. As a Staff Software Engineer, you will play a crucial role in the development of our Managed Kubernetes platform, specifically designed for AI workloads. You will collaborate with various teams, drive technical vision, and shape the infrastructure that powers the next generation of AI training and inference at scale. This is a foundational technical leadership role that requires expertise in distributed systems, GPU-accelerated computing, and Cloud Native infrastructure.

• Conduire la vision technique de la plateforme Kubernetes gérée de Lambda, y compris la scalabilité du plan de contrôle, la multi-location, la gestion du cycle de vie des clusters et la haute disponibilité.
• Intégrer et étendre l'écosystème open-source de NVIDIA, y compris le GPU Operator, le Network Operator, le DCGM, le NCCL, et les projets émergents pour la planification et le placement.
• Diriger le développement de services qui alimentent nos services gérés, informer et aider avec les solutions de mise en réseau et d'architecture de stockage pour les charges de travail d'IA.

• You embrace modern tools and AI-assisted development (like Claude Code) to accelerate your productivity and multiply your impact
• You find elegant solutions and ship them quickly
• You don't just solve problems
• You are a creative, innovative engineer who operates at high velocity
• You're energized by building new things, not maintaining the status quo
• Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems
• Expert-level understanding of Kubernetes internals: API machinery, controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful
• Proven track record of technical leadership: driving design decisions across teams, mentoring engineers, and influencing infrastructure direction beyond your immediate scope
• 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
• Solid knowledge of Linux systems and networking (L2-L7), including high-performance networking concepts (RDMA, InfiniBand, RoCE)
• 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
• Strong software engineering skills in Go (required) and Python; you write production-quality code, not just scripts
• 10+ years of experience in software engineering, platform engineering, or SRE, with at least 5 years focused on Kubernetes at scale
• Strong understanding of distributed systems principles: consensus, fault tolerance, consistency models, and graceful degradation
• Experience with infrastructure-as-code and GitOps workflows
• Deep experience designing and operating managed services or multi-tenant platforms. You understand what it takes to run infrastructure for external customers
• 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