2026 Machine Learning Research Intern

Lambda•California, PA

51d•Onsite

About The Position

Lambda, The Superintelligence Cloud, builds Gigawatt-scale AI Factories for Training and Inference. Lambda’s mission is to make compute as ubiquitous as electricity and give every person access to artificial intelligence. One person, one GPU. If you'd like to build the world's best deep learning 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. Join Lambda as a Machine Learning Research Intern and help advance the frontiers of generative AI. You’ll collaborate closely with world-class researchers and engineers, leveraging Lambda’s compute to optimize model and system performance. Our interns work hands-on across two complementary tracks, Fundamental Research and Applied Research, depending on their interests and experience. What You’ll Work On Foundation Models, Multi-Modal, Agents System benchmarking and performance optimization Track 1: Fundamental Research Research in foundation models for language, vision, life sciences and robotics. Multi-modal research, including building efficient data and evaluation toolkits. Publish findings in top-tier ML Research conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV/ECCV, ACL, SIGGRAPH), and/or as technical blogs, public datasets, benchmarks, and open-source tools. Track 2: Applied Research Maximize training and inference performance for large-scale AI systems. Systematic model and agent evaluation. Publish findings in top-tier ML system conferences (e.g. MLSys, OSDI, SOSP, NSDI), and/or as technical blogs, public dataset, benchmarks, and open-source tools.

Requirements

BS, MS, or Ph.D. student in Computer Science or related field, focusing on Machine Learning.
Demonstrated project experience or publications in relevant areas.
Proficient in PyTorch or similar frameworks.
Strong communication and collaboration skills.

Nice To Haves

Contributions to open-source machine learning projects.
Experience with foundation models, multi-modal, agentic systems.
Experience with dataset creation, evaluation design, or system benchmarking.
Experience optimizing model efficiency or scaling ML workloads.

Responsibilities

Research in foundation models for language, vision, life sciences and robotics.
Multi-modal research, including building efficient data and evaluation toolkits.
Publish findings in top-tier ML Research conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV/ECCV, ACL, SIGGRAPH), and/or as technical blogs, public datasets, benchmarks, and open-source tools.
Maximize training and inference performance for large-scale AI systems.
Systematic model and agent evaluation.
Publish findings in top-tier ML system conferences (e.g. MLSys, OSDI, SOSP, NSDI), and/or as technical blogs, public dataset, benchmarks, and open-source tools.