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Full-Time

Senior HPC Systems Engineer

Confirmed live in the last 24 hours

Lambda

Lambda

51-200 employees

Cloud-based GPU services for AI training

Data & Analytics
Hardware
Enterprise Software
AI & Machine Learning

Compensation Overview

$180k - $250kAnnually

Senior, Expert

Remote in USA + 1 more

Category
Deep Learning
AI & Machine Learning
Required Skills
Kubernetes
Python
Linux/Unix
Requirements
  • Have expertise with architecting, operating, and debugging large scale HPC network and storage infrastructure, ideally using MPI, NCCL, RDMA, Infiniband, and parallel file systems
  • Are experienced with building complex, high-quality software using Python
  • Possess a deep understanding of Linux fundamentals, especially its networking stack
  • Have experience with large GPU clusters is strongly preferred
  • Have experience with virtualization and kubernetes
  • Come from a strong engineering background - Computer Science, Electrical Engineering, Mathematics, Physics
Responsibilities
  • Design and architect the state-of-the-art AI supercomputers powering our cloud
  • Introduce technology and software to improve the performance, resiliency, and quality of service of our HPC storage and networking infrastructure
  • Work closely with our ML team to benchmark, tune, and optimize our hypervisors, network, and storage
  • Set up monitoring, logging and alerting to ensure high availability and observability
  • Provide guidance and represent the interests of our HPC customers

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, 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.

Company Stage

Series C

Total Funding

$932.2M

Headquarters

San Jose, California

Founded

2012

Growth & Insights
Headcount

6 month growth

30%

1 year growth

67%

2 year growth

242%
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Simplify's Take

What believers are saying

  • Lambda Labs' competitive pricing and availability have attracted high-profile clients like Voltron Data, indicating strong market demand.
  • The recent $500M GPU-backed financing facility will enable Lambda to expand its cloud infrastructure significantly, enhancing service capabilities.
  • The appointment of Peter Seibold as CFO, with his extensive experience, is likely to strengthen Lambda's financial strategy and operational efficiency.

What critics are saying

  • The rising prices of AI cloud compute instances could deter cost-sensitive clients, impacting Lambda's customer acquisition.
  • The competitive landscape, with giants like AWS launching high-core instances, poses a threat to Lambda's market share.

What makes Lambda unique

  • Lambda Labs leverages NVIDIA's GH200 Grace Hopper™ Superchip, offering unmatched efficiency and price for AI training and inference, setting it apart from competitors.
  • Their Lambda Stack software simplifies AI-related software installation and upgrades, used by over 50,000 machine learning teams, providing a significant edge in user experience.
  • The Lambda Echelon service allows clients to take ownership of their infrastructure, a unique offering compared to traditional cloud service models.