Senior Site Reliability Engineer - Fleet Reliability at Lambda

San Francisco, California, United States

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$155,000 – $224,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, TechnologyIndustries

Requirements

7+ years in Site Reliability Engineering, DevOps, or a similar role
Strong understanding of modern AI infrastructure (GPU architectures, hardware performance optimization)
Solid understanding of Linux-based systems in a distributed environment
Proficiency in Python and Go, with experience working with Software Engineering teams
Experience with monitoring and alerting tools (e.g., Prometheus, Grafana, SumoLogic)
Proficiency in automation and configuration management tools (e.g., Ansible, Terraform)
Familiarity with cloud platforms (e.g., OCI, AWS, GCP, Azure)
Excellent problem-solving and troubleshooting skills
Strong communication and collaboration skills
Passion for continuous improvement and innovation

Responsibilities

Define Fleet Health metrics and indicators to objectively measure and improve system availability
Collaborate with the observability team on comprehensive monitoring and alerting systems to proactively predict, detect, and respond to issues or anomalies
Create runbooks and automated remediations for common failure scenarios
Build in automation and auditing to ensure compliance and improve efficiency and productivity
Participate in on-call rotations and provide support for incident response and resolution
Implement and integrate logging and metrics across platforms such as Datadog, Prometheus, OpenTelemetry, Grafana, SumoLogic, etc

Skills

Python

Linux

Prometheus

Grafana

SumoLogic

Ansible

Terraform

AWS

GCP

Azure

OCI

Datadog

OpenTelemetry

GPU

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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