Senior Software Engineer - Managed Kubernetes

San Francisco, California, United States

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$255,000 – $405,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Cloud Computing, Software EngineeringIndustries

Requirements

Candidates should possess 6+ years of experience in software engineering or SRE roles, with at least 3+ years of leadership in large-scale complex projects or as a tech lead. They must have experience tuning Kubernetes internals and writing operators (CRDs, CSI, CNI, etc.), strong programming skills in Go and Python, proficiency in GitOps (e.g., ArgoCD), Helm, and Kubernetes operators, and experience operating Kubernetes clusters in production environments (e.g., EKS, GKE, on-prem). A deep understanding of SRE principles, including incident response, chaos engineering, scaling, and reliability, is also required, along with proficiency in observability tools (Prometheus, Grafana, FluentBit, etc.) and experience with infrastructure-as-code tools (Terraform, Pulumi) and CI/CD pipelines.

Responsibilities

The Senior Software Engineer will design, build, and maintain scalable control plane services, operators, and custom controllers for Kubernetes, develop automation for cluster lifecycle management, and create internal tools, APIs, and CLIs to enable customers to deploy and monitor inference services effectively. They will write resilient systems that handle failure in large-scale distributed environments, define and implement SLOs and SLIs for Kubernetes services, dive into systems to solve cluster problems, assist customers with Kubernetes questions, and participate in an on-call rotation. Additionally, they will contribute to initial cluster build-outs and validation, work with HPC and Datacenter Ops teams, and potentially contribute to CNCF projects or Kubernetes SIGs.

Skills

Kubernetes

Control Plane Services

Operators

Custom Controllers

Automation

Cluster Lifecycle Management

APIs

CLI Development

Distributed Systems

Site Reliability Engineering (SRE)

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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