Staff Engineer, AI Cloud Orchestration (Managed Kubernetes)

Lambda Inc.; San Francisco (CA); Hybrid; USD 190,000 - 270,000; Full time

Benefits offered by this job

Health, dental, and vision coverage

Wellness and commuter stipends

401k with company match

Job summary

Lambda is building the AI cloud for scale and reliability. We seek a Staff Engineer to lead the Orchestration platform, shaping the infrastructure that powers AI training and inference at scale.

You will own technical direction for Managed Kubernetes on bare metal, integrate NVIDIA tooling, and drive platform services for multi-tenant workloads while collaborating across networks, storage, and security teams.

Qualifications

• 10+ years in software or platform engineering with Kubernetes at scale.
• Expert-level understanding of Kubernetes internals and operators.
• Holistic infrastructure knowledge across compute, networking, storage, and security.
• Strong Go and Python production-grade coding skills.
• Deep experience with GPU orchestration in Kubernetes and NVIDIA tooling.
• Proven leadership driving infrastructure decisions across teams.

Responsibilities

• Define technical direction for Kubernetes services across the Orchestration team.
• Lead reviews and design sessions to ensure scalable, maintainable systems.
• Mentor engineers and establish best practices for cloud-native engineering.
• Collaborate with Network, Storage, Security, and Customer Success teams.
• Engage with NVIDIA and open-source communities to stay current.
• Shape AIOps vision for capacity planning and predictive maintenance.

Skills

Kubernetes at scale

Go

Python

Distributed systems

GPU orchestration

NVIDIA GPU Operator

Networking (RDMA/InfiniBand)

Tools

Kubernetes

Slurm on Kubernetes

NVIDIA Network Operator

CNI (Calico/Flannel)

GPUDirect

Prometheus/Grafana

Job description

Lambda is building the AI cloud for scale and reliability. We seek a Staff Engineer to lead the Orchestration platform, shaping the infrastructure that powers AI training and inference at scale.

You will own technical direction for Managed Kubernetes on bare metal, integrate NVIDIA tooling, and drive platform services for multi-tenant workloads while collaborating across networks, storage, and security teams.