Lambda
AI cloud infrastructure
FieldEngineeringIntern-Summer2026
Neural analysis suggests this role is
optimal for Entry candidates.
“Field Engineering Intern - Summer 2026 at Lambda. Skills: ML inference, model optimization, applied ML deployment, machine learning model architecture, MLOps, fine-tuning models. Optimize, deploy, and scale ML workloads on GPU infrastructure. Work hands-on with customers”
What You'll Achieve.
Quantify the value of field engineering work in a repeatable, scalable format; Present your work to company leadership at the close of the engagement
Industry & Context.
Debug from scratch; Evaluate strategies against current best practices; Recommend improvements
Requires presence in our San Francisco office location 4 days per week
What They're Looking For.
Must Have
Currently pursuing or just completed a Master's degree in Computer Science, Machine Learning, or a related field, Python skills with hands-on experience in ML inference, model optimization, benchmarking / evaluations, or applied ML deployment., Solid background and general knowledge of machine learning model architecture, Skillset to be able to write code (without any AI assistance) to build an ML model and debug from scratch., Understand how models run in production – MLOps tools, open-source models, orchestration strategies., Understanding of fine-tuning models., Curious and keep up to date with new models, techniques, strategies, and releases in machine learning, Can write clearly for both technical and non-technical audiences, Comfortable using Claude or equivalent AI tools as a core part of your daily workflow, Self-directed
Nice to Have
Familiarity with LLM inference optimization frameworks (vLLM, sgLang, Modular, TensorRT-LLM, or similar), Able to write tests to create layer-wise benchmarking for ML model performance, Familiarity with networking, storage, and various orchestration tools / methods., Prior internship at an ML infrastructure, cloud, or GPU hardware company, Interest in or prior exposure to customer-facing engineering, solutions engineering, or developer relations
What You'll Do.
and scale ML workloads on GPU infrastructure
Work hands-on with customers
Partner with enterprise
and on-demand customers on ML use cases
Dig into real customer optimization work
Help build the foundation that lets us scale
Support customer onboarding
optimization engagements
and production deployments
Review prior optimization work
Evaluate strategies against current best practices
Recommend improvements
Develop a structured optimization playbook and case studies
Quantify the value of field engineering work
How You'll Work.
Team & Collaboration
Embed with the Field Engineering team; Learn directly from ML engineers who made the transition to customer-facing field engineering
Communication Scope
Write clearly for both technical and non-technical audiences; Translating results is as important as producing them; Present your work to company leadership
Process & Methodology
Break into milestones and drive to completion
Full 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 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 - Currently pursuing o
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