---
title: 'Senior Site Reliability Engineer - Storage at Lambda'
canonical: 'https://feeny.ai/job/senior-site-reliability-engineer-storage-lambda-san-francisco-0fs6kv27req1'
type: 'job'
last_seen: '2026-09-06'
---

# Senior Site Reliability Engineer - Storage at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-10
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/lambda/d910e6b0-300f-4ebc-89f5-ba5adc9a3bfe

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

Lambda's Storage Engineering team is the backbone behind our world-class storage offerings, operating at a scale seldom seen in today's markets. We own the full spectrum of Lambda's data platform services—from low-level storage systems to the APIs and tooling our customers build on every day.

Our work directly powers some of the most demanding compute workloads in the industry, which means reliability and performance aren't just goals—they're the baseline. We're looking for engineers who want to solve problems at scale, own critical systems end to end, and help shape the future of infrastructure for AI and beyond

## What You’ll Do

- Own the reliability, performance, and capacity health of Lambda's production storage fleet across all data centers, operating behind Lambda's own software-defined data plane.
- Build and maintain monitoring, dashboards, and alerting for storage performance, capacity, and hardware failures.
- Investigate and resolve storage-related incidents using deep telemetry, logs, and performance profiling — from a single flapping NIC to a cluster-wide rebuild.
- Automate ticketing, escalation, and incident-response workflows so the team spends less time on repetitive triage and more time on root cause.
- Design and maintain self-healing automation for common failure modes: drive replacement, node swaps, rebuild monitoring, and capacity rebalancing.
- Implement CI/CD pipelines for storage automation and tooling.
- Partner with Storage Engineers, Fleet Orchestration, and Release Engineering to automate the deployment and configuration of software-defined storage across new and existing sites using tools such as Ansible, Jenkins etc.
- Work with hardware and networking teams to diagnose low-level I/O and network issues — NIC errors, RDMA/RoCE/InfiniBand fabric health, path multipathing — that surface as storage-layer symptoms.
- Participate in an on-call rotation supporting Lambda's storage fleet, with a focus on driving down MTTR and building the automation that keeps you from getting paged for the same thing twice.

## YOU HAVE

- 5+ years of experience operating Linux systems in production or HPC environments, with hands-on storage experience at scale on scale-out or software-defined platforms (e.g., CEPH, Lustre, GPFS, or similar).
- Hands-on experience operating Software-Defined Storage (SDS) platforms at scale, including integrating with their management and data-plane APIs.
- Strong incident-response instincts: comfortable owning a production storage incident end to end, from first alert through root cause to postmortem.
- Working experience with monitoring and logging platforms such as Prometheus, Grafana, Alertmanager, Datadog, or SumoLogic — including building dashboards and alert/pager routing for multiple audiences.
- Working experience with Kubernetes (GitOps tooling such as ArgoCD, Helm/Kustomize) and hands-on troubleshooting.
- Working experience with CI/CD tooling (GitHub Actions, Jenkins, BuildKite), containerization (Docker/Podman), and systems programming in Python or Go.
- Working experience with Infrastructure as Code (Terraform, Ansible).
- Solid understanding of core storage protocols across file (NFS, SMB), object (S3), block (NVMe-oF/TCP), and structured (vector DB, SQL) storage.

## Nice to Have

- Experience with cutting-edge software-defined storage solutions such as VAST or Weka.
- Enterprise storage expertise in solutions such as NetApp, Dell PowerScale, GPFS, or Lustre.
- Experience writing or operating Kubernetes CSI drivers.
- Experience with SR-IOV and virtualization (KVM/QEMU).
- Experience with GPUDirect Storage, RDMA, InfiniBand, or RoCE networking.
- Familiarity with NIC-level diagnostics (ethtool, mlxlink) and fleet-wide operations tooling (clush or similar).
- Contributions to open-source storage projects.

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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