3 months ago
Base Salary
$226k - $355k/yr
Responsibilities
- Partner with Account Executives to lead complex enterprise cloud deals and build relationships with CTOs, AI/ML leaders, and platform teams.
- Evaluate customer architectures, identify bottlenecks, and design end-to-end GPU cloud solutions.
- Author proposals, architecture diagrams, Bills of Materials, and rack elevations for multi-node GPU clusters.
- Lead hands-on proof-of-concept activities, custom prototypes, and benchmark evaluations for training and inference workloads.
- Architect and optimize AI/ML workloads across data ingestion, distributed training, inference, networking, storage, and observability.
- Advise customers on Kubernetes, SLURM, vLLM, TensorRT-LLM, InfiniBand, RoCE, and high-performance GPU infrastructure.
- Represent the technical voice of customers internally and communicate product gaps and feature requests to Product and Engineering.
- Create technical whitepapers, field enablement assets, architectural blueprints, and workshops, and represent Lambda at conferences and webinars.
Requirements
- 8+ years of experience designing, deploying, and scaling enterprise cloud infrastructure.
- 4+ years in a Solution Architect, Solution Engineer, or technical customer-facing role supporting complex cloud environments.
- 3+ years of hands-on experience architecting and deploying cloud-based AI/ML workloads.
- Experience deploying, benchmarking, and optimizing workloads on NVIDIA GPU architectures, including HGX platforms and NVLink, with PyTorch, NeMo, vLLM, and TensorRT-LLM.
- Strong experience with Kubernetes, Docker, SLURM, Terraform, and Ansible.
- Deep knowledge of cloud networking, InfiniBand, RoCE, distributed file systems, security, and cost optimization.
- Programming experience in Python, Go, C, C++, CUDA, or a similar language.
- Experience partnering with Account Executives, presenting technical architectures to C-level stakeholders, and driving customer alignment.
- Demonstrated organizational or multi-departmental impact and experience mentoring junior solutions engineers or architects.
- Nice-to-have experience includes LLM fine-tuning, algorithm selection, pipeline design, distributed training, 3D parallelism, Megatron-LM, product launches, GTM initiatives, technical whitepapers, benchmarks, RESTful APIs, gRPC, and service-oriented cloud architectures.
Benefits
- The role requires working from the San Francisco, San Jose, or Bellevue office four days per week, with Tuesday currently designated as the work-from-home day.
- Lambda offers cash and equity compensation, health, dental, and vision coverage, wellness and commuter stipends for select roles, a 401(k) plan with a 2% company match for U.S. employees, and flexible paid time off.
About Lambda
Lambda provides GPU cloud computing and on-prem AI hardware—servers, clusters, and workstations—for teams training and serving large ML models. Its products include NVIDIA H100/A100 instances, managed clusters, and the Lambda Stack software, sold via usage-based cloud pricing and hardware sales. Founded in 2012 and headquartered in San Francisco, the privately held company serves researchers, startups, enterprises, and hyperscalers.