---
title: 'Data Center Standards Architect at Lambda'
canonical: 'https://feeny.ai/job/data-center-standards-architect-lambda-san-jose-2n0c06hnexe6'
type: 'job'
last_seen: '2026-09-06'
---

# Data Center Standards Architect at Lambda

- **Company:** Lambda
- **Location:** San Jose, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-14
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/lambda/87d1ca1b-1618-4db6-b411-450dfb3ea781

## Job description

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.

If you'd like to build the world's best AI cloud, join us.

Note: This position requires presence in our San Francisco office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.

## What You’ll Do

As a Data Center Standards Architect, you will serve as a principal-level technical leader responsible for designing the standards, systems, tools, and processes that help Lambda scale its data center infrastructure. This role is ideal for someone who has led engineering teams or large technical programs and now wants to operate as a high-leverage individual contributor shaping how a fast-growing infrastructure organization works.

You will:

- Define and own Lambda’s data center engineering standards for high-density AI and GPU infrastructure environments.
- Create repeatable reference architectures, design patterns, technical specifications, and deployment standards that improve speed, quality, reliability, and consistency across data center builds.
- Develop the engineering “operating system” for data center scale, including standards libraries, design review processes, decision records, quality gates, commissioning criteria, acceptance checklists, exception processes, and operational handoff frameworks.
- Translate lessons learned from individual deployments into reusable mechanisms that make future deployments safer, faster, and more predictable.
- Establish technical standards across areas such as power distribution, cooling, liquid cooling readiness, rack integration, structured cabling, fiber management, network rooms, out-of-band management, telemetry, DCIM/BMS integrations, physical security, maintainability, and operational readiness.
- Lead cross-functional architecture reviews for new data center designs, expansions, retrofits, and infrastructure programs.
- Identify organizational bottlenecks and design systems, tools, and workflows that reduce ambiguity, improve accountability, and help teams execute at scale.
- Build governance mechanisms for standards adoption, including exception management, risk reviews, lifecycle ownership, metrics, and continuous improvement loops.
- Act as a technical advisor to senior engineering and infrastructure leadership on data center strategy, design tradeoffs, resiliency, operational risk, and scalability.
- Mentor engineers and technical leaders across the organization by raising the bar for systems thinking, documentation, design rigor, and operational excellence.

You

- Have operated as a staff, principal, or director-level engineering leader in data center engineering, infrastructure engineering, facilities engineering, cloud infrastructure, hardware infrastructure, or a related technical domain.
- Have experience designing or scaling standards, processes, tools, or governance systems across a complex engineering organization.
- Bring a strong systems-thinking mindset and are comfortable turning ambiguous, one-off problems into repeatable, scalable mechanisms.
- Have deep familiarity with data center infrastructure, including power, cooling, rack layouts, network infrastructure, cabling, commissioning, operational readiness, and lifecycle management.
- Understand how to balance engineering excellence with delivery velocity, cost, risk, reliability, and operational simplicity.
- Are comfortable influencing without direct authority across engineering, operations, construction, supply chain, finance, and executive stakeholders.
- Communicate clearly through written standards, technical narratives, architecture diagrams, design reviews, and executive-level recommendations.
- Have a track record of raising the technical bar for teams through better processes, better documentation, better decision-making frameworks, and better engineering discipline.
- Are energized by building the systems that allow other engineers and operators to move faster and make better decisions.
- Care deeply about reliability, maintainability, repeatability, and operational excellence in mission-critical infrastructure environments.

## Nice to Have

- Experience with AI, machine learning, GPU, HPC, or hyperscale cloud infrastructure.
- Experience with high-density compute environments, liquid cooling, advanced thermal management, or next-generation data center design.
- Experience building or operating infrastructure standards across multiple sites, regions, colocation providers, or global data center portfolios.
- Familiarity with DCIM, BMS, EPMS, telemetry platforms, infrastructure observability, or workflow automation tools.
- Experience creating internal platforms, tooling, dashboards, or knowledge systems that help engineering organizations scale.
- Experience working with colocation providers, OEMs, ODMs, construction partners, commissioning agents, and critical facilities operations teams.
- Experience leading technical governance forums, architecture review boards, standards councils, or large-scale engineering transformation programs.
- Background in electrical engineering, mechanical engineering, systems engineering, network engineering, infrastructure architecture, or a related field.

Salary Range Information

The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

## About Lambda

- Founded in 2012, with 500+ employees, and growing fast
- Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove
- We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
- Our values are publicly available: https://lambda.ai/careers
- We offer generous cash & equity compensation
- Health, dental, and vision coverage for you and your dependents
- Wellness and commuter stipends for select roles
- 401k Plan with 2% company match (USA employees)
- Flexible paid time off plan that we all actually use

## Equal Opportunity Employer

Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

## About Lambda

## Company Overview
- **One-liner**: Lambda builds supercomputers and cloud infrastructure for training and deploying large-scale AI models, from single GPUs to gigawatt-scale AI factories.
- **Entity Type**: Private (funding stage not publicly disclosed; founded by ML engineers)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2012
- **Founders**: Stephen Balaban and Michael Balaban

## Core Business
- Primary industry: AI infrastructure / cloud computing for machine learning.
- Target customers: Frontier AI labs building large foundation models, hyperscalers scaling global AI infrastructure, and enterprises deploying AI in regulated industries (B2B, Enterprise).
- Mission: “Make compute as ubiquitous as electricity and give everyone in America the power of superintelligence” (also “One person, one GPU”).

## Products & Services
- **The Superintelligence Cloud**: A suite of cloud computing offerings specifically built for AI workloads, including:
  - **GPU Instances**: On-demand NVIDIA HGX B200, H100, and GB300 NVL72 instances for prototyping and testing.
  - **Managed Clusters**: Dedicated, single-tenant clusters (e.g., NVIDIA GB300 NVL72, HGX B200/H100) with full management and co-engineering from Lambda’s team.
  - **1-Click Clusters™**: Rapidly deployable clusters for training and inference.
  - **Superclusters**: Large-scale AI factories integrating high-density power, liquid cooling, and high-bandwidth interconnects.
- **AI Infrastructure Hardware**: Modular AI factory designs and NVIDIA-based systems for on-premise or colocation deployment.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Total funding not publicly available; revenue not disclosed.
- **Notable Customers/Partners**: “World’s most advanced AI organizations” (frontier labs, hyperscalers, regulated enterprises). Leadership includes former executives from cloud and networking companies.
- **Growth Signals**: Active hiring across engineering, storage, security, and procurement roles; building AI factories at gigawatt scale; SOC 2 Type II certified; expanding from San Francisco to San Jose, CA.

## Competitive Advantages
- **AI‑First DNA**: 100% of engineering, operations, and support dedicated to AI – founded by ML engineers in 2012.
- **Single‑Tenant Isolation**: Shared‑nothing architecture for security and performance, with hardware‑level isolation.
- **Full‑Stack Expertise**: Co‑engineering from the same team building the infrastructure, enabling deep optimization for large training runs.
- **Hacker Culture**: Rooted in the Noisebridge hackerspace values of do‑ocracy, low ego, and “be excellent to each other.”
- **Performance**: Rack‑scale NVIDIA systems (GB300, B200, H100) with high‑speed interconnects (NVIDIA Quantum‑2 InfiniBand).

## Strategic Focus
- Scaling infrastructure to support the next generation of superintelligence, including gigawatt‑scale AI factories.
- Enabling frontier labs to train trillion‑parameter models and serve billions of tokens in production.
- Expanding compliance and security capabilities for regulated industries.
- Growing the cloud platform (The Superintelligence Cloud) as the primary go‑to‑market offering.

## Why Work Here
- **Culture**: Hacker ethos (Noisebridge roots), low ego, no yelling, no politics, no crypto. Values: build, move fast, care, be excellent to each other.
- **Work Environment**: Fast‑paced, high‑change, outcome‑focused. Emphasis on technical excellence and curiosity. Anonymous feedback encouraged.
- **Interview Process**: Clear, structured steps (recruiter chat → hiring manager → technical assessment → panel interviews → reference/offer). Pedigree is not everything; focus on what you’ve built.
- **Location & Remote**: Offices in San Francisco and San Jose, CA. FAQ page addresses remote/hybrid policy (details not provided in available snippets); some roles appear on‑site.
- **Perks**: Benefits, time off, and other perks are listed on the careers site (specifics not extracted here).

## Sources
1. [lambda.ai/about](https://lambda.ai/about)
2. [lambda.ai/](https://lambda.ai/)
3. [lambda.ai/careers](https://lambda.ai/careers)
4. [lambda.ai/leadership](https://lambda.ai/leadership)

## Other roles at Lambda

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- [Data Center Construction Site Foreman (Dallas)](https://feeny.ai/job/data-center-construction-site-foreman-dallas-lambda-united-states-vc7fg15nb275) — United States
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