---
title: 'Engineering Manager, Fleet Engineering at Lambda'
canonical: 'https://feeny.ai/job/engineering-manager-fleet-engineering-lambda-san-francisco-3peym7jpkty9'
type: 'job'
last_seen: '2026-09-13'
---

# Engineering Manager, Fleet Engineering at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-22
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.ashbyhq.com/lambda/b47d567c-f99a-4d0f-a128-8e6e42297ac5

## 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, San Jose, or Bellevue office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.

## About the Role

Fleet Engineering owns the full lifecycle of Lambda's production systems infrastructure — new product introduction, deployment, operation, and reliability of our GPU fleet. We enable the building and running of that infrastructure with speed, ease, and quality. The Fleet Engineering teams:

- HPC Deployments — Turns bare metal into production-ready capacity: ensure firmware leveling, system burn-in to shake out early failures, and validation of server performance and correctness, through to the InfiniBand fabric and GPU clusters.
- Fleet Reliability — Day-2 operations across the fleet. Keeps systems healthy and keeps as much of the fleet in service for as much of its useful life as possible.
- Fleet Orchestration / Data — Owns our production source of truth system. Synchronizes data from upstream systems and holds the line on correctness and quality, because everything automated downstream depends on it.
- Fleet Orchestration / Automation — Owns the workflow orchestration system which people use to safely work on fleet systems for workflows that include: locking hosts, running firmware leveling jobs, OS installs, burn-in and validation, and reporting on work in flight and its results.
- Fleet Foundation — Builds the host enablement tooling systems: OS and ZTP switch provisioning, firmware management, out-of-band access, and power management.

The work is highly cross-functional, carries executive visibility, and has a direct impact on Lambda and our customers. Fleet Engineering is at the forefront of delivering on-time, high-quality GPU capacity while driving efficiency at scale. We are hiring multiple Engineering Managers for the following teams: Fleet Reliability, HPC Deployments, Fleet Foundation, Fleet Orchestration / Automation. This is a single application for all of them: you apply once, we get to know you, and we match you to the team where your strengths land best. We value diverse backgrounds, experiences, and skills, and we're excited to hear from candidates who bring a unique perspective. If you don't exactly meet this description but believe you may be a good fit, please still apply and help us understand your readiness for this role. Your application is not a waste of our time.

## What You'll Do

- Lead and grow a distributed team of top-talent engineers responsible for the deployment and operation of production systems infrastructure.
- Work cross-functionally to deliver projects and deployments on time, ensuring alignment across stakeholders.
- Identify opportunities for efficiency gains in the tools, processes, and automation that teams across the organization rely on day to day.
- Give stakeholders clear visibility into project progress, risks, and outcomes.
- Participate in qualification efforts for new technologies entering our production deployments.
- Drive outcomes by managing staff allocation, project priorities, deadlines, and deliverables.
- Hold regular 1:1s, give constructive feedback, and support career development for your team.
- Contribute to reliability through participation in our Incident Management and Review programs.

You

- Have 3+ years leading or managing engineers, in AI/ML infrastructure or another large-scale compute environment.
- Have owned production systems with real SLAs, and can balance keeping things running against long-term, high-impact work — paying down toil and technical debt along the way.
- Work confidently in Linux and can debug across the OS, hardware, and networking layers.
- Can lead technical design on medium-to-large efforts: take an ambiguous problem, write the doc, drive alignment across teams, and ship.
- Work well under deadlines and structured project plans, and can tactfully negotiate changes to timelines when reality demands it.
- Collaborate effectively with peer engineering managers on efforts that cut across deployment and operations.
- Build high-performing teams deliberately — through hiring, upskilling, planned skills redundancy, performance management, and clear expectations.
- Have excellent problem-solving and troubleshooting instincts.
- Are excited about working at the intersection of hardware, software, and physical datacenter builds.
- Leave systems, and the teammates around you, better than you found them.

## Nice to Have

Depth in any one of these is a strong signal, and helps us place you on the right team. Nobody has all of them.

- Linux systems administration, TCP/IP networking, automation, and scripting.
- Bare metal provisioning and lifecycle management — PXE, Redfish, IPMI, BMC, DHCP, DNS.
- Strong coding ability in at least one language, plus comfort with APIs, distributed systems, and automation pipelines.
- The technologies underpinning our cloud business: GPU acceleration, virtualization, cloud computing.
- Datacenter physical infrastructure: racks, switches, InfiniBand fabric, power domains.
- Network source-of-truth or DCIM tooling (NetBox or similar), and data quality practice at scale.
- Building Linux distributions, or managing OS customization and imaging.
- Incorporating AI-assisted development tools into engineering workflows — code generation, debugging, test development, documentation.
- Customer awareness, empathy, and diplomacy.
- Bachelor's degree or equivalent experience in a technical 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)

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