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Field Engineering Intern - Summer 2026

Lambda · San Francisco Office (Second St)

Hybrid Intern
vLLMOrchestrationGPUTensorRTPythonComputer visionDeep learningNLPLatencyThroughputPyTorchQuantizationCI/CDKubernetesDockerAWSGCPAzureMCPObservabilityEval harnessesLLM-as-judgeTool useRAGHugging Face

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 office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. The Field Engineering team is a group of ML engineers working hands-on with customers to optimize, deploy, and scale ML workloads on the most advanced GPU infrastructure available. We partner with enterprise, YC, and on-demand customers on some of the most demanding ML use cases in the industry and we're growing. This summer, we're looking for an ML engineering intern to embed with the team, dig into real customer optimization work, and help build the foundation that lets us scale. If you want hands-on experience at the intersection of cutting-edge ML and real-world customer impact, this is the role. What You'll Do - Learn directly from ML engineers who made the transition to customer-facing field engineering, gaining firsthand exposure to how deep ML expertise translates into real-world customer impact - Work on real, cutting-edge customer workloads running on the most advanced GPU infrastructure available, supporting customer onboarding, optimization engagements, and production deployments across some of the most demanding ML use cases in the industry - Review prior optimization work, evaluate strategies against current best practices, and recommend improvements - Develop a structured optimization playbook and case studies that capture the team's methodology and quantify the value of field engineering work in a repeatable, scalable format - Present your work to company leadership at the close of the engagement You

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