Machine Learning Intern

About CantinaCantina Labs is a social AI company developing a suite of advanced video generation models. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.About the InternshipCantina is growing its research lab in Singapore, and we are looking for exceptional machine learning interns to work with us on the next generation of video models in October 2026.This is a three month onsite internship designed to give you meaningful ownership of a well-defined research or engineering problem. You will be matched with a project based on your background and interests, working closely with a senior mentor from initial problem formulation through experimentation, evaluation, and, where appropriate, submission to a leading AI conference.Projects may focus on post-training and inference efficiency for video generation models, reward modeling and preference-based optimization, multimodal data systems, or scalable infrastructure for video model training. The primary focus will be your core project, with opportunities to contribute to applied or product-adjacent work where relevant.What You’ll Work OnDepending on your project, you may:Develop reward models to improve aesthetics, motion quality, temporal consistency, and prompt adherenceStudy how base-model behavior affects post-training outcomes and use experimental findings to inform model developmentDesign rigorous evaluations and conduct large-scale experiments on generative video modelsBuild systems for ingesting, preprocessing, curating, and delivering large-scale video datasetsDevelop distributed pipelines for dataset generation, deduplication, preprocessing, and repeated dataset refreshesImprove the reliability, reproducibility, and efficiency of data and model-training workflowsBuild tooling for video and multimodal data using technologies such as FFmpeg, PyAV, DALI, or OpenCVContribute to evaluation harnesses, model integrations, research tooling, or other product-adjacent projects related to your core workDocument and communicate your findings through research reports, internal presentations, demonstrations, and potential conference submissionsYou may be a good fit if youAre pursuing a bachelor’s or master’s degree in computer science, engineering, machine learning, or a related fieldHave experience building data pipelines, ML infrastructure, or distributed systems through research, coursework, open-source contributions, or previous internshipsAre familiar with tools such as Ray, PySpark, Airflow, Docker, Kubernetes, or equivalent technologiesHave worked with cloud storage or compute platforms such as AWS, Google Cloud, or AzureUnderstand practical considerations around data throughput, storage layout, caching, monitoring, and failure recoveryAre proficient in Python and interested in building reliable systems for large-scale machine learningExperience with video, image, audio, or other multimodal data is valuable. Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or AAAI are a plus, but are not required. We care most about the quality of your thinking, the depth of your technical work, and your ability to learn quickly.What You Can ExpectA defined project and named senior mentor before your first dayWeekly one-on-one meetings and clear project milestonesA meaningful compute allocation for your researchThe opportunity to own a complete research or engineering resultFirst-author positioning by default where your contribution supports a publicationTimely internal review of research intended for submissionSupport for conference travel if your paper is acceptedOpportunities to demonstrate your work and receive credit for product contributionsA competitive monthly stipendVisa and travel support for eligible international candidatesHousing support for qualifying international interns in SingaporeEquipment and resources needed to complete your workInternship DetailsLocation: SingaporeDuration: Three monthsWorking model: OnsiteStart dates: Start dates: First batch starts in October 2026; second batch starts in January 2027