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

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.

What You’ll Do

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

You

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)

Deep understanding of Kubernetes internals: controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful

Solid grasp of distributed systems fundamentals — fault tolerance, graceful degradation, and failure handling in large-scale environments

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

Strong programming skills in Go and Python; ability to collaborate effectively on shared codebases

Solid knowledge of Linux systems, networking, containers, and cloud infrastructure

Take pride in owning and delivering core components of products and platforms