We are seeking a Senior Software Engineer to join our Managed Kubernetes (Mk8s) team
You will play a crucial role in shaping the architecture, reliability, and automation of our Kubernetes-based infrastructure, which powers mission-critical workloads across our global platform
Lambda is building the AI Cloud of the future
We are seeking a Senior Software Engineer to help our development of our Managed Kubernetes platform
Think GKE, but purpose-built for AI workloads and running on bare metal
In this role, you will help build the infrastructure that powers the next generation of AI training and inference at scale
As a Senior Engineer on our Orchestration team, you will contribute to 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’s a role for an engineer who understands how compute, network, storage, and security interact, and can build solutions that account for that context — even while focused primarily on the orchestration layer
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
Design, build, and maintain scalable control plane services, operators, and custom Kubernetes controllers; develop automation in Go/Python for end-to-end cluster lifecycle management — provisioning, upgrades, patching, and deletion
Build GPU-aware orchestration systems, working within the platform architecture to support GPU scheduling and resource allocation
Partner with the Network team on networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect
Write resilient systems that handle failure gracefully — timeouts, retries, backoff, and degraded-mode operation — across large-scale distributed environments
Develop platform services for inference: model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns
Build internal tools and CLIs that let ML/AI teams deploy and monitor their own inference services
Support and debug production issues through on-call rotation
Benefits
Health, dental vision
401k match
5 sick days and 12 paid holidays
Flexible PTO
Paid parental, medical, and caregiver leave- Solid knowledge of Linux systems, networking, containers, and cloud infrastructure
Strong programming skills in Go and Python; ability to collaborate effectively on shared codebases
Solid grasp of distributed systems fundamentals — fault tolerance, graceful degradation, and failure handling in large-scale environments
Deep understanding of Kubernetes internals: controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful
Experience operating the control plane and low-level pieces of large-scale Kubernetes clusters
Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems
Take pride in owning and delivering core components of products and platforms
Have 6+ years of experience in software engineering, with a track record of owning significant technical scope within a team (e.g., driving a project from design through production, or acting as a de facto tech lead on a workstream)
If you don’t meet all of these requirements but believe you may be a good fit, please still apply and provide a cover letter that helps us understand your experience and readiness for this role
Past contributions to CNCF projects or Kubernetes SIGs a plus
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
Experience building and operating managed Kubernetes services (GKE, EKS, AKS, or similar) or working on Kubernetes control plane components
Exposure to storage architecture for AI/ML workloads
Hands-on experience with NVIDIA’s GPU/networking ecosystem: GPU Operator, device plugins, DCGM, MIG, Network Operator, NCCL tuning, or similar
