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