---
title: 'Senior Solution Engineer at Lambda'
canonical: 'https://feeny.ai/job/senior-solution-engineer-lambda-san-francisco-hrsx6etewq65'
type: 'job'
last_seen: '2026-10-18'
---

# Senior Solution Engineer at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-03
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/lambda/061cf79b-0e95-4082-8805-5822499441f1

## 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.

The Lambda Cloud GTM team powers our growth by enabling customers to realize their business goals with AI infrastructure. We partner with leading AI researchers and enterprise engineering teams to design, scale, and optimize high-performance GPU cloud solutions. Driven by technical mastery, agility, and a customer-first mindset, our team turns massive compute challenges into seamless, production-ready AI infrastructure.

## What You’ll Do

- Drive technical sales & executive influence
- Partner with Account Executives to lead complex deals with large enterprises and digital native businesses and build trusted relationships with technical leaders (CTOs, Heads of AI/ML, Platform Leads)
- Evaluate customer architectural needs, uncover potential bottlenecks, and design end-to-end GPU cloud solutions
- Author comprehensive proposals & architecture diagrams and collaborate with teams on Bill of Materials (BOMs), and rack elevations for multi-node GPU clusters
- Lead hands-on proof-of-concept (PoC) activities & benchmarking for customers
- Design, execute, and deliver technical PoCs and custom prototypes to demonstrate Lambda’s performance, reliability, and value
- Run benchmark evaluations across training and inference workloads to show tangible performance and cost advantages over competitors
- Architect & optimize AI/ML workloads
- Guide enterprise engineering teams on structuring their AI lifecycle—from data ingestion and distributed training (SLURM, Kubernetes) to inference optimization (vLLM, TensorRT-LLM) and observability
- Provide architectural guidance on high-performance networking (InfiniBand, RoCE), distributed storage, and cluster topologies to ensure maximum GPU utilization
- Champion customer feedback & product advocacy
- Serve as the technical voice of the customer internally, funneling field insights, product gaps, and feature requests directly to Lambda’s Product and Engineering teams
- Create field enablement assets, technical whitepapers, architectural blueprints, and lead technical workshops for prospective clients & partners
- Represent Lambda as a subject matter expert at industry conferences, webinars, and technical community events
- Reinforce Lambda’s culture
- Contribute positively throughout the organization
- Maintain a high level of agility and responsiveness
- Hyper-focused on customer satisfaction

You

- Have a proven track record deploying, benchmarking, and optimizing workloads on NVIDIA GPU architectures (e.g., HGX platforms, NVLink) using deep learning frameworks (PyTorch, NeMo) and inference engines (vLLM, TensorRT-LLM)
- Have 8+ years of experience designing, deploying, and scaling enterprise cloud infrastructure
- Have 4+ years in a Solution Architect, Solution Engineer, or technical customer-facing capacity supporting complex cloud environments
- Have 3+ years of hands-on experience architecting and deploying cloud-based AI/ML workloads
- Have strong experience with modern infrastructure orchestration tools such as Kubernetes, Docker, SLURM, Terraform, and Ansible
- Have deep knowledge of cloud networking concepts, including high-speed interconnects (InfiniBand, RoCE), distributed file systems (NFS, NVMe-oF, Weka, VAST), security, and cost optimization
- Have experience coding in Python, Go, C/C++ (CUDA) or similar programming language
- Have experience partnering with Account Executives to close complex cloud deals, present technical architectures to C-level stakeholders (CTOs, VP of Eng), and drive customer alignment
- Have demonstrated impact at an organizational/multi-departmental level and are effective mentoring junior SEs or architects
- Thrive in dynamic settings and embrace radical ownership of initiatives and outcomes

## Nice to Have

- Direct experience with end-to-end LLM fine-tuning, algorithm selection, pipeline design, or distributed training setups (3D parallelism, Megatron-LM)
- Prior experience with product launches, leading GTM initiatives, or publishing technical whitepapers/benchmarks
- Experience integrating RESTful APIs, gRPC, and service-oriented cloud architectures

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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