Machine Learning Intern
At a glance
Junior Machine Learning Engineering role at Cantina. Singapore · internship.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build reward models that improve video aesthetics, movement, temporal consistency and alignment with prompts
- Run evaluations and large experiments to test how base-model behavior shapes post-training results
- Create pipelines to ingest, prepare, curate and refresh large video datasets
- Improve the reliability and efficiency of data and training workflows, including tools for video and multimodal data
- Share results through reports, presentations or demonstrations, and contribute to conference submissions when appropriate
What you bring
- You are pursuing a bachelor's or master's degree in computer science, engineering, machine learning or a related area
- You have built data pipelines, ML infrastructure or distributed systems through study, research or practical work
- You can code in Python and want to build dependable systems for large-scale machine learning
Who this fits
This junior internship suits someone who wants to own a defined research or engineering project with guidance from a senior mentor. It is an onsite role in Singapore lasting three months, with start dates in October 2026 or January 2027. Visa and travel support is available to eligible international candidates.
From the employer
About Cantina
Cantina 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 Internship
Cantina 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 On
Depending on your project, you may:
Develop reward models to improve aesthetics, motion quality, temporal consistency, and prompt adherence
Study how base-model behavior affects post-training outcomes and use experimental findings to inform model development
Design rigorous evaluations and conduct large-scale experiments on generative video models
Build systems for ingesting, preprocessing, curating, and delivering large-scale video datasets
Develop distributed pipelines for dataset generation, deduplication, preprocessing, and repeated dataset refreshes
Improve the reliability, reproducibility, and efficiency of data and model-training workflows
Build tooling for video and multimodal data using technologies such as FFmpeg, PyAV, DALI, or OpenCV
Contribute to evaluation harnesses, model integrations, research tooling, or other product-adjacent projects related to your core work
Document and communicate your findings through research reports, internal presentations, demonstrations, and potential conference submissions
You may be a good fit if you
Are pursuing a bachelor’s or master’s degree in computer science, engineering, machine learning, or a related field
Have experience building data pipelines, ML infrastructure, or distributed systems through research, coursework, open-source contributions, or previous internships
Are familiar with tools such as Ray, PySpark, Airflow, Docker, Kubernetes, or equivalent technologies
Have worked with cloud storage or compute platforms such as AWS, Google Cloud, or Azure
Understand practical considerations around data throughput, storage layout, caching, monitoring, and failure recovery
Are proficient in Python and interested in building reliable systems for large-scale machine learning
Experience 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 Expect
A defined project and named senior mentor before your first day
Weekly one-on-one meetings and clear project milestones
A meaningful compute allocation for your research
The opportunity to own a complete research or engineering result
First-author positioning by default where your contribution supports a publication
Timely internal review of research intended for submission
Support for conference travel if your paper is accepted
Opportunities to demonstrate your work and receive credit for product contributions
A competitive monthly stipend
Visa and travel support for eligible international candidates
Housing support for qualifying international interns in Singapore
Equipment and resources needed to complete your work
Internship Details
Location: Singapore
Duration: Three months
Working model: Onsite
Start dates: Start dates: First batch starts in October 2026; second batch starts in January 2027