---
title: 'Senior Software Engineer - Core Cloud Platform at Lambda'
canonical: 'https://feeny.ai/job/senior-software-engineer-core-cloud-platform-lambda-san-francisco-076g9nn4jkzn'
type: 'job'
last_seen: '2026-09-20'
---

# Senior Software Engineer - Core Cloud Platform at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-15
- **Last confirmed live:** 2026-09-20
- **Apply:** https://jobs.ashbyhq.com/lambda/35c68922-7d43-430c-83f4-86690dbff216

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

## About the Role

As a Senior Software Engineer on Lambda’s Core Cloud Platform team, you will build the control-plane systems that power Lambda’s GPU cloud. The team owns core platform capabilities across compute lifecycle, bare metal orchestration, deployment readiness, reliability, and operational tooling. You will architect systems that turn bare-metal GPU infrastructure into reliable, customer-facing cloud capacity. This means building orchestration layers, distributed schedulers, and automated control loops to manage instance lifecycles, host reclaims, and safe worldwide deployments. This role is a strong fit for engineers who enjoy distributed systems, cloud infrastructure, operational excellence, and working close to the hardware/software boundary.

## What You’ll Do

- Architect and scale foundational cloud platform services that govern compute lifecycles, bare-metal hosts, capacity planning, and intelligent placement workflows for a high-performance GPU cloud.
- Raise the bar on operational readiness by treating observability, and alerting as software engineering problems.
- Eliminate toil by engineering automated, self-healing control loops for bare-metal lifecycle events (launch, reboot, host reclaim, validation, and quarantine).
- Design and maintain highly resilient APIs, backend services, and distributed state machines, ensuring fault tolerance and high availability for business-critical control planes.
- Lead incident response and resolution for complex production issues across distributed services, infrastructure dependencies, and networking, driving blameless post incident analysis to ensure systemic improvements.
- Partner cross-functionally with networking, storage, security, fleet, and product teams to define clear cross-system contracts and deliver seamless end-to-end cloud capabilities.
- Drive engineering excellence by contributing to system architecture, authoring detailed design docs, conducting rigorous code reviews, and mentoring engineers across the team.

You May Be a Good Fit If You

- Have 6+ years of professional software engineering experience building highly available distributed systems in Python, Go, or a similar language.
- Have designed and operated complex systems like distributed state machines, orchestrators, workflow engines, or high-throughput APIs on modern infrastructure (Linux, Kubernetes).
- Possess a deep understanding of reliability fundamentals, including fault tolerance, state machines, idempotency, and graceful degradation.
- Bring an engineering approach to operations, treating on-call as an opportunity to relentlessly automate away manual toil.
- Thrive in ambiguity, acting as a technical leader who can turn complex infrastructure challenges into elegant, resilient production outcomes.

Nice-to-Haves

- Experience building cloud control planes, event-driven architecture, or durable workflows (Temporal, Airflow).
- Track record of implementing Infrastructure as Code (IaC), deep observability (Prometheus, distributed tracing), and automated incident remediation.
- Hands-on knowledge of VPCs, SDN, InfiniBand, routing, or fundamental security concepts like tenant isolation and identity management.
- Experience with bare metal provisioning, GPU clusters, large-scale AI/ML infrastructure, or host lifecycle management (BMC/Redfish).

You Will Be Successful in This Role If You

- Enjoy breaking down ambiguous platform problems into concrete APIs, workflows, contracts, and implementation plans.
- Collaborate seamlessly across product, infrastructure, fleet, networking, security, and support boundaries.
- Care deeply about production behavior, not just shipping a feature.
- Are genuinely excited to help build the foundational cloud platform layer that powers large-scale AI infrastructure.

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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