Senior Software Enginering - Mk8S/Control Plane

Lambda; United States; Hybride; USD 150 000 - 260 000; Plein temps

Résumé du poste

Lambda is seeking a Senior Software Engineer to join the Mk8s team, helping build a Kubernetes-based AI cloud platform. You will design and scale control plane services, drive automation, and contribute to the orchestration layer for AI workloads run on bare metal.

You will work at the intersection of distributed systems and GPU-accelerated infrastructure, collaborating with internal teams to deliver reliable, high-performance services for real-world AI training and inference workloads.

Qualifications

• Experience designing and building scalable control plane services.
• Proficiency with Go or Python for automation.
• Experience with Kubernetes and distributed systems.

Responsabilités

• Design and maintain scalable control plane services and Kubernetes controllers.
• Develop GPU-aware orchestration and scheduling components.
• Collaborate on networking solutions and platform services for inference.

Connaissances

Go

Python

Kubernetes

Distributed systems

GPU scheduling

Outils

CNI (Cilium, Multus)

GPUDirect

InfiniBand/RDMA

Description du poste

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 service