Senior Platform Engineer at Lambda

San Francisco, California, United States

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$240,000 – $401,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud ComputingIndustries

Requirements

5+ years of experience in Platform, Infrastructure, or SRE roles
Expert knowledge of Kubernetes internals and operational practices
Proven experience running Kubernetes clusters in production at scale
Strong skills with Helm, Kustomize, or similar deployment tooling
Solid understanding of networking, service meshes, and container runtimes
Proficiency in infrastructure-as-code (Terraform, Pulumi, etc.)
Experience with observability stacks (Prometheus, Grafana, ELK, OpenTelemetry)
Familiarity with security best practices (network policies, secrets management, image scanning)
Strong coding skills in Go, Python, or similar for automation
Comfort with GitOps workflows and CI/CD integration
Excellent problem-solving skills and ability to operate in complex environments

Responsibilities

Architect, deploy, and manage Kubernetes clusters across AWS, OCI, and on-prem datacenters
Build and maintain automation for cluster lifecycle management, upgrades, and scaling
Own the reliability, performance, and security of Kubernetes workloads
Implement observability, logging, and alerting for clusters and critical workloads
Partner with developers to design scalable, cloud-native services and CI/CD pipelines
Define and enforce best practices for resource usage, networking, and RBAC
Lead incident response, root cause analysis, and post-mortems for cluster-related issues
Mentor junior engineers and contribute to internal platform engineering standards

Skills

Kubernetes

AWS

OCI

Helm

Kustomize

Terraform

Pulumi

Networking

Service Meshes

Container Runtimes

CI/CD

Observability

Logging

Alerting

Infrastructure as Code

RBAC

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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