---
title: 'Senior Product Security Engineer at Lambda'
canonical: 'https://feeny.ai/job/senior-product-security-engineer-lambda-san-francisco-y8m9pzn8e06r'
type: 'job'
last_seen: '2026-11-08'
---

# Senior Product Security Engineer at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-11-04
- **Last confirmed live:** 2026-11-08
- **Apply:** https://jobs.ashbyhq.com/Lambda/140ec386-5d50-4cfb-b24c-eeb2b0c08afc

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

## About the Role

Lambda Security protects some of the world's most valuable digital assets: training data, model weights representing immense computational investments, and the sensitive inputs required to leverage best-of-breed AI models. We're responsible for securing every byte that powers breakthrough artificial intelligence.

As a Senior Security Engineer on Product Security, you'll make sure the right security decisions get made in everything Lambda sells, from the network and cluster infrastructure in our data centers to the software our customers use every day. Reporting to the Senior Manager of Security, you'll join a team spanning infrastructure security and application security, owning security outcomes for your area and partnering closely with Product Engineering, Data Center Operations and Engineering, Detection & Response, and GRC.

Product security at Lambda generates and protects revenue. Enterprises and AI labs evaluate our security before they move billion-dollar training runs and irreplaceable model weights onto our infrastructure, and this team supplies the answers. As Lambda launches new products and expands into new data centers, you'll threat model and review the designs behind each one, keep every customer's workloads isolated from the network fabric up, run vulnerability management as a risk-driven program, and bring technical depth to the customer security reviews behind enterprise deals. You'll work with direct access to LLMs hosted on Lambda's own infrastructure, alongside frontier models and AI tools, to take code and design review further than traditional approaches allow.

Product security covers a lot of ground, and we don't expect one person to be an expert in all of it. Some of our team go deep on infrastructure: networks, clusters, Linux and the hardware underneath. Others go deep on application security and code. We expect you to bring real depth in one and contribute across both. If you'd rather build security into systems alongside the engineers who own them than audit them from the outside, we'd love to talk.

We value diverse backgrounds, experiences, and skills, and we are excited to hear from candidates who can bring unique perspectives to our team. If you do not 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

- Review Designs and Threat Model: Own security design reviews and threat models for the systems in your area, delivering actionable recommendations while changes are still cheap.
- Secure Multi-Tenant Infrastructure: Harden the network, cluster, and infrastructure-service layers that keep each customer's workloads isolated from every other customer's.
- Find and Fix Vulnerabilities in Code: Review application code and infrastructure as code, find vulnerabilities through analysis and testing, and land fixes with the owning team.
- Run Vulnerability Management as a Program: Turn scanner output into accurate, prioritized facts about real risk, and drive remediation to SLA alongside our Technical Program Managers.
- Develop Security Patterns and Requirements: Turn recurring review findings into requirements, reference patterns, and guardrails that the teams you support adopt by default.
- Enable Enterprise Deals: Partner with GRC on customer security reviews and architecture discussions, giving customers clear, technically grounded answers about how we protect their workloads.
- Build Security Tooling: Ship Python or Go automation, using Lambda-hosted LLMs, frontier models, and AI tools, that extends review and triage coverage.
- Partner Across Engineering: Work with Product Engineering, Data Center Operations and Engineering, and Detection & Response, including on bug bounty and vulnerability disclosure reports.
- Coach Engineers: Help engineers in the teams you support build secure design instincts, and mentor newer members of the security team.
- Balance Strategic and Tactical: Recognize when to invest in a long-term fix versus when "good enough" is exactly that, and bias toward measurable forward progress.

You

- 5+ years of demonstrated security engineering experience, either directly as a security engineer or as a software, infrastructure, or site reliability engineer driving security outcomes. We also welcome equivalent backgrounds: significant formal security training paired with strong engineering experience.
- Demonstrated depth in infrastructure security or application security, with design reviews, threat models, or fixes that engineering teams actually adopted.
- A track record of independently owning security work end to end, making sound calls on your own and knowing when to escalate.
- Thrives in high-speed, high-ambiguity startup environments where priorities shift regularly and structure must be built while executing.
- Strong hands-on Linux experience in both bare metal and cloud environments, understanding infrastructure from kernel to application layer.
- Reads application code and infrastructure as code (e.g., Terraform, Ansible, Kubernetes manifests) fluently enough to explain a vulnerability's root cause, not just a scanner's verdict.
- Ships production-grade tooling in Python, Go, or a similar language, rather than only one-off scripts.
- Translates security risk into business impact for engineers, leadership, and customers, enabling them to make informed decisions.
- Excellent collaboration with technical teams both with and without authority (we're all on the same team!).

## Nice to Have

- Deep code analysis expertise, such as source code auditing, fuzzing, reverse engineering, or vulnerability research with published findings or CVEs.
- Experience securing multi-tenant infrastructure: network segmentation, virtualization or container isolation, Kubernetes, or hardware root of trust and secure boot.
- You've led the security assessment and requirements for a major platform component or enterprise system.
- Experience building infrastructure-as-code security guardrails, such as policy-as-code checks in CI/CD or secure-by-default modules.
- You've run vulnerability management as a program, with risk-based prioritization and remediation SLAs that held.
- Experience supporting enterprise customer security reviews, or providing significant evidence for compliance audits such as SOC 2, ISO 27001, or FedRAMP.
- Experience with AI/ML infrastructure security, model security, or protecting high-value computational workloads.
- Significant experience operating large-scale production services (e.g., SRE across thousands of hosts) or with virtualization at scale (KVM, Hyper-V, Xen).
- Enthusiasm about leveraging LLMs on Lambda's own infrastructure, frontier models, and AI tools to push product security beyond the status quo, including through AI-assisted code review, automated threat modeling, and intelligent vulnerability triage.

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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