Senior Site Reliability Engineer - Managed Kubernetes at Lambda

San Francisco, California, United States

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$267,000 – $401,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud ComputingIndustries

Requirements

6+ years of experience in a SRE, operations engineer, or similar role, with a deep knowledge of running Linux clusters and systems
Strong programming skills in Go and Python; experience with GitOps (e.g., ArgoCD), Helm, and Kubernetes operators
Proven experience operating Kubernetes clusters in production environments (on-prem, EKS, GKE, or similar)
Can work either independently with limited direction or as part of a team
Can work with customers during incidents either via tickets, live messaging, or as part of a larger call
Familiarity with observability tools like Prometheus, Grafana, FluentBit, and CI/CD pipelines
Proven experience provisioning Kubernetes using tools such as kubeadm, Cluster API, or similar
Nice-to-Have
Deep Kubernetes expertise: CRDs, CSI, CNI, Kubernetes Operator Coding experience
Exposure to HPC clusters, AI/ML workloads, or large-scale GPU clusters
Hybrid or multi-cloud Kubernetes environment experience
Contributions to CNCF projects or Kubernetes SIGs

Responsibilities

Operate and maintain bare-metal Kubernetes clusters, scaling up to thousands of nodes
Handle cluster degradation, recovery, resizing, and incident response using fleet management tools
Participate in a well-managed on-call rotation for critical incidents
Assist customers with Kubernetes questions, workload integration, storage, and authentication
Work closely with our HPC Ops and Datacenter Ops teams for low-level or cross-functional issues
Use Python and Golang to create tooling and automate the validation of platform quality
Design, build, and maintain scalable control plane services, operators, and custom controllers for Kubernetes
Develop automation for cluster lifecycle management: provisioning, upgrades, patching, and deletion
Define and implement SLOs and SLIs for Kubernetes services, workloads, and platform reliability

Skills

Key technologies and capabilities for this role

KubernetesGoPythonLinuxArgoCDHelmPrometheusGrafanaFluentBitGitOps

Questions & Answers

Common questions about this position

What is the salary range for the Senior Site Reliability Engineer position?

The salary range is $267K - $401K.

Is this role remote or hybrid, and what's the location policy?

The position is hybrid.

What are the must-have skills for this Senior SRE role?

Must-have skills include 6+ years in SRE or operations with Linux clusters, strong Go and Python programming, Kubernetes production experience (on-prem, EKS, GKE), GitOps (ArgoCD), Helm, operators, observability tools like Prometheus and Grafana, and customer support during incidents.

What is the company culture like at Lambda?

Lambda offers a fast-paced environment focused on massive AI deployments, working with smart minds on world-changing projects, collaboration with highly skilled engineers, and opportunities to influence the platform roadmap in a technology-driven setting.

What makes a strong candidate for this Senior SRE position?

Strong candidates have 6+ years SRE experience with Linux and Kubernetes in production, proficiency in Go/Python, GitOps/Helm/operators, observability tools, and customer-facing skills; nice-to-haves include deep Kubernetes expertise, HPC/AI workloads, and CNCF contributions.

Lambda

Cloud-based GPU services for AI training

About Lambda

Lambda Labs provides cloud-based services for artificial intelligence (AI) training and inference, focusing on large language models and generative AI. Their main product, the AI Developer Cloud, utilizes NVIDIA's GH200 Grace Hopper™ Superchip to deliver efficient and cost-effective GPU resources. Customers can access on-demand and reserved cloud GPUs, which are essential for processing large datasets quickly, with pricing starting at $1.99 per hour for NVIDIA H100 instances. Lambda Labs serves AI developers and companies needing extensive GPU deployments, offering competitive pricing and infrastructure ownership options through their Lambda Echelon service. Additionally, they provide Lambda Stack, a software solution that simplifies the installation and management of AI-related tools for over 50,000 machine learning teams. The goal of Lambda Labs is to support AI development by providing accessible and efficient cloud GPU services.

San Jose, CaliforniaHeadquarters

2012Year Founded

$372.6MTotal Funding

DEBTCompany Stage

AI & Machine LearningIndustries

201-500Employees

Risks

Nebius' holistic cloud platform challenges Lambda's market share in AI infrastructure.

AWS's 896-core instance may draw customers seeking high-performance cloud solutions.

Existential crisis in Hermes 3 model raises concerns about Lambda's AI model reliability.

Differentiation

Lambda offers cost-effective Inference API for AI model deployment without infrastructure maintenance.

Nvidia HGX H100 and Quantum-2 InfiniBand Clusters enhance Lambda's AI model training capabilities.

Lambda's Hermes 3 collaboration showcases advanced AI model development expertise.

Upsides

Inference API launch attracts enterprises seeking low-cost AI deployment solutions.

Nvidia HGX H100 clusters provide competitive edge in high-performance AI computing.

Strong AI cloud service growth indicates rising demand for Lambda's GPU offerings.

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