Field Engineering Intern, Lambda

• Currently pursuing or just completed a Master's degree in Computer Science, Machine Learning, or a related field
• Strong Python skills with hands-on experience in ML inference, model optimization, benchmarking / evaluations, or applied ML deployment
• Have a solid background and general knowledge of machine learning model architecture
• You have the skillset to be able to write code (without any AI assistance) to build an ML model and debug from scratch
• You understand how models run in production – MLOps tools, open-source models, orchestration strategies
• You have a strong understanding of fine-tuning models
• Are curious and keep up to date with new models, techniques, strategies, and releases in machine learning and are driven to bring these insights to your work
• Can write clearly for both technical and non-technical audiences, translating results is as important as producing them
• Comfortable using Claude or equivalent AI tools as a core part of your daily workflow
• Self-directed: given a scoped problem and a mentor, you can break it into milestones and drive it to completion

Desirable

• 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
• 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