---
title: 'Staff Product Manager - Observability at Lambda'
canonical: 'https://feeny.ai/job/staff-product-manager-observability-lambda-bellevue-5t09nw1b5snq'
type: 'job'
last_seen: '2026-10-18'
---

# Staff Product Manager - Observability at Lambda

- **Company:** Lambda
- **Location:** Bellevue, WA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-14
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/lambda/cb7fe04b-0c99-4dbc-afa6-4a05a9c8cac1

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

## About the Role

A customer runs distributed training across 64 to 1,024+ GPUs (Graphics Processing Units). When a job slows down, customers need one question answered instantly: is it my code, NCCL (NVIDIA Collective Communications Library) configuration issue, or a bad InfiniBand link? Observability is how Lambda answers that question. It lets customers distinguish their code, their configuration, and our platform in minutes, and that transparency is a core reason teams trust Lambda with their largest training runs. This role makes that transparency real.

You will be the product manager who owns observability as a product across Lambda's cloud. That means everything customers see: GPU and cluster health, utilization, job-level telemetry, and InfiniBand fabric metrics. It also means the platform telemetry layer underneath, which powers Lambda's internal fleet operations and reliability work. You will partner daily with SRE (Site Reliability Engineering), fleet engineering, and the console and API (Application Programming Interface) teams to bring one coherent observability experience to customers and operators alike. This role reports to the Head of Platform Product Management.

Great product managers at Lambda are defined by three things: insight, influence, and execution. Insight means you look at the data, determine what it means for customers and business, and then figure out what to do about it. But, a great idea doesn’t mean anything in a vacuum. That is where influence comes in. Influence means you take that idea and get others to want to buy into it; you win over engineers, designers, executives, and partners without relying on authority. But a great idea that everyone is excited about doesn’t matter unless it is delivered to customers. Execution means you work with the right people to get the idea launched, then measure and iterate. We hire product managers who learn new domains fast and reason rigorously from evidence. Deep observability domain experience is a strong plus, but insight, influence, and execution are the bar.

If you love turning raw telemetry into products customers rely on, and you want your work to be the reason a research team trusts their 1,024-GPU training run, we'd love to hear from you. 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.

## What You'll Do

- Own the Observability Roadmap: Define and drive the product strategy for observability across Lambda's cloud, from single On-Demand GPU Instances to 1-Click Clusters at 64 to 1,024+ GPU scale.
- Turn Telemetry into Product: Decide which signals customers see, including GPU and cluster health, utilization, job-level telemetry, and InfiniBand fabric metrics, and how they see them in the console and the Lambda Cloud API.
- Ship Diagnostics That Answer the First Question: Build experiences that let a customer quickly distinguish their own code or configuration issues from platform problems such as a failing node or a degraded InfiniBand link.
- Work Backwards: One of the key things customers want to know is if their application is causing issues or if it is Lambda. If it is Lambda, they want to know if it is within the expected tolerance. You will work with each product to define and productize SLIs, SLOs, and SLAs.
- Build the Platform Telemetry Layer: Partner with SRE and fleet engineering to define the internal telemetry that powers fleet operations and reliability, so one data foundation serves both customers and operators.
- Live with Customers: Spend time with AI research labs, AI startups, and enterprise ML (machine learning) teams to understand how they debug distributed training and inference, and turn that signal into decisions about what to build next.
- Win the Room: Bring engineers, designers, and executives along on your roadmap through clear writing and evidence, not authority.
- Measure and Iterate: Define the success metrics for observability features, instrument adoption, and use the results to decide what ships next.
- Deliver with the Console and API Teams: Drive observability capabilities through to launch across the console, the Lambda Cloud API, and cluster tooling, so customers can consume telemetry however they work.

You

- Have 7+ years of product management experience, including 3+ years on technical infrastructure, platform, or developer-facing products
- Turn data and customer signal into a clear decision about what to build next, and can show examples where your insight changed a roadmap
- Win over engineers, designers, executives, and customers without formal authority, and have shipped products that required cross-team adoption
- Have taken products from idea through launch, measurement, and iteration, and can speak to what you learned from the metrics after shipping
- Are comfortable in deeply technical conversations about distributed systems, and can hold your own with engineers on topics like metrics pipelines, hardware health, and failure modes
- Can write crisply, so a one-page document from you is enough to align a room
- Are energized by ambiguity, and can be the first product manager to own a domain and give it shape
- Able to define iterative plans that move an organization from the current state towards the desired outcome

## Nice to Have

- Have shipped monitoring or observability products, such as those in the Datadog, Grafana, or Prometheus class
- Have worked with HPC (high performance computing) or distributed systems telemetry, including interconnect and fabric-level metrics
- Have built developer tools or API-first products
- Have hands-on exposure to distributed training stacks such as PyTorch with NCCL, or to GPU fleet tooling such as DCGM (Data Center GPU Manager)
- Have worked in a usage-based cloud infrastructure business

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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