Staff Engineer, AI Cloud Kubernetes (Bare Metal)

Showcify, Inc.; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

NVIDIA partnership

Equity compensation

Flexible PTO

Job summary

Lambda is seeking a Staff Engineer to lead the design and delivery of its managed Kubernetes bare-metal platform, focusing on HPC workloads, multi-tenant concerns, and integration with NVIDIA open-source tooling. You will guide infrastructure across compute, network, storage and security while partnering with NVIDIA and internal teams.

You will mentor engineers, influence roadmaps, and shape the platform to support AI training and inference workloads at scale, with a focus on reliability and

Qualifications

• 10+ years in software/platform engineering or SRE, with Kubernetes at scale
• Deep understanding of Kubernetes internals: API machinery, controllers, schedulers, CRDs, CSI, CNI
• Holistic infra experience across compute, network, storage, and security
• Strong Go and Python development skills; production-quality coding
• GPU orchestration experience: NVIDIA GPU Operator, device plugins, DCGM, MIG, scheduling

Responsibilities

• Set technical direction for Kubernetes services across the Orchestration team
• Mentor engineers and drive design decisions across teams
• Collaborate with Network, Storage, Security to build multi-tenant platforms
• Contribute to NVIDIA ecosystem and open-source community
• Shape AIOps vision: capacity planning, anomaly detection, maintenance

Skills

Kubernetes

Go

Python

Distributed systems

GPU orchestration

Tools

NVIDIA GPU Operator

DCGM

CNI (Cilium, Multus)

Prometheus

Grafana

Job description

Lambda is seeking a Staff Engineer to lead the design and delivery of its managed Kubernetes bare-metal platform, focusing on HPC workloads, multi-tenant concerns, and integration with NVIDIA open-source tooling. You will guide infrastructure across compute, network, storage and security while partnering with NVIDIA and internal teams.

You will mentor engineers, influence roadmaps, and shape the platform to support AI training and inference workloads at scale, with a focus on reliability and