[Remote] HPC Support Engineer at Lambda

United States

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Not SpecifiedCompensation

N/AExperience Level

N/AJob Type

Not SpecifiedVisa

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Requirements

7+ years in cloud support operations or systems engineering
Strong experience with public cloud platforms (AWS, Azure, GCP) or GPU cloud providers
Very strong understanding and experience with Linux (Ubuntu) system administration
Proven experience in HPC environments, showcasing expertise in Linux cluster administration, with strong preference for Kubernetes and/or Slurm
Proficiency with monitoring/logging tools (Prometheus, Grafana, Datadog)
Strong skills in log analysis, debugging kernel-level issues, and performance profiling
Experience with CUDA, NCCL, NVLink, MIG, GPUDirect RDMA
Experience with high throughput networking technologies (IB/RoCE)
Experience with virtualization and container (Docker, Kubernetes) technologies
Knowledge of distributed AI/ML or HPC workloads
Knowledge of TCP/IP, VPN, and firewalls in cloud environments

Responsibilities

Engage directly with customers to deeply understand their challenges, ensuring a personalized, and effective support experience
Dive into complex software and hardware issues, providing timely and efficient solutions
Craft comprehensive documentation of solutions and contribute to enhancing support procedures, ensuring continuous improvement in service quality
Identify common customer pain points and collaborate closely with engineering teams to develop innovative solutions, constantly improving the overall customer experience
Collaborate in the development of new and existing products, contributing expertise to shape the future of deep learning cloud and HPC infrastructure
Take escalations from peers while looking for opportunities to train and educate them
Work cross-functionally on project work, focusing on creating and improving support tooling
Participate in a rotating on-call schedule for major incidents and customer alerts

Skills

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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