[Remote] AI Infrastructure Deployment Lead at Lambda

United States

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$128,000 – $149,000Compensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Computing, Data CentersIndustries

Requirements

Bachelor’s degree in Computer Engineering, Information Technology, or related field
CCNA (Cisco Certified Network Associate) certification (CCNP or equivalent a plus)
PMP (Project Management Professional) Certification (PMP or equivalent a plus)
5+ years of experience in data center infrastructure deployment or network operations, preferably in AI, HPC, or cloud environments
Proven ability to lead complex technical projects and manage multidisciplinary teams
Strong understanding of data center network design (Layer 2/3, VLAN, Rack elevations, port mapping, InfiniBand technologies)
Hands-on expertise in server hardware troubleshooting and rack-level integration
Preferred
Experience deploying or managing GPU clusters and distributed training environments
Familiarity with automation and orchestration tools (Ansible, Terraform) and monitoring systems (Prometheus, Grafana)
Knowledge of structured cabling, power distribution, and environmental monitoring in data centers
Excellent communication skills

Responsibilities

Lead end-to-end deployment of GPU clusters, storage systems, and networking fabric across Lambda’s data centers
Design and implement data center network topologies optimized for AI and HPC workloads, including high-speed Ethernet and InfiniBand environments
Oversee rack implementation, cabling, and power/cooling validation for optimal efficiency and scalability
Collaborate with supply chain, logistics, and operations teams to ensure smooth delivery and installation timelines
Implement Layer 2/Layer 3 networks, including VLANs, Spine to Leaf architecture, InfiniBand interconnect technology
Partner with network architects to ensure redundancy, scalability, and low-latency interconnects for distributed AI workloads
Monitor network health, identify bottlenecks, and implement optimizations to maintain peak performance
Oversee server hardware troubleshooting, including GPUs, NICs, CPUs, and storage components
Lead root-cause analysis for system issues and drive corrective actions in collaboration with vendors and internal hardware teams
Develop standard operating procedures (SOPs) for hardware validation, deployment, and maintenance
Serve as technical project lead for infrastructure rollouts and cluster expansion projects
Coordinate cross-functional teams — networking, facilities, cloud operations, and hardware engineering — to execute deployments on schedule
Manage project scope, budgets, risk assessments, and post-deployment reviews
Communicate status, challenges, and milestones to leadership with clarity and precision
Maintain detailed network topology diagrams, deployment runbooks, and hardware inventories
Identify opportunities for process automation and infrastructure standardization across deployments
Contribute to Lambda’s internal knowledge base and mentor junior engineers on data center best practices

Skills

Key technologies and capabilities for this role

GPU clustersInfiniBandEthernetSpine-Leaf architectureLayer 2/Layer 3 networksVLANsdata center networkingHPC workloadsserver hardware troubleshootingGPUsNICsCPUsstorage systemsrack integrationcablingpower/cooling validation

Questions & Answers

Common questions about this position

What is the salary range for the AI Infrastructure Deployment Lead role?

The salary range is $128K - $149K.

Is this position remote?

Yes, this is a remote position.

What key skills are required for this role?

The role requires expertise in GPU cluster deployment, data center network topologies including Ethernet and InfiniBand, Layer 2/Layer 3 networking, server hardware troubleshooting for GPUs/NICs/CPUs/storage, and technical project leadership.

What is the company culture like at Lambda?

Lambda focuses on building the world's best deep learning cloud with a mission to make compute ubiquitous like electricity, emphasizing innovation in AI factories for training and inference.

What makes a strong candidate for this position?

Strong candidates combine hands-on technical expertise in hardware, networking, and systems design with strategic project leadership, thriving at the intersection of these areas and experienced in leading cross-functional teams for large-scale deployments.

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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