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Job Title: AI Video Research Engineer Intern
Company: Tether.io
Location: Georgia
Description:
We are seeking highly motivated MSc or PhD interns to work on video generation and multimodal video foundation models. Interns will focus on one or more components of the foundation model lifecycle and are encouraged to propose creative, research-driven ideas that advance the state of the art. You will contribute to the development and improvement of open-source video foundation models, analyze their limitations, and design scalable solutions. This is a research-focused internship with opportunities to publish at top-tier computer vision and machine learning conferences, and to work with petabyte-scale video datasets and large distributed GPU clusters with thousands of GPUs.
Responsibilities:
* Research and improve open-source video and multimodal video generation foundation models
* Focus on one or more areas such as pre-training, supervised fine-tuning, post-training, inference, architecture design, or evaluation
* Benchmark models against current state-of-the-art, identify bottlenecks, and propose novel improvements
* Work with large-scale video datasets and distributed training systems
* Collaborate with researchers and engineers on projects with clear research and publication potential
Minimum Qualifications:
* MSc or PhD candidate in Computer Science, Machine Learning, Computer Vision, or a related technical field
* Research topic or experience in image generation, video generation, or multimodal learning
* Awareness of open-source video foundation models and their current limitations
* Proficiency with PyTorch and modern deep learning workflows
* Strong analytical thinking, creativity, and collaboration skills
* Prior first-author related publications in CVPR, ICCV, ECCV, NeurIPS, or ICLR
Preferred Qualifications:
* Demonstrated related work, such as research codebase or benchmarks released on GitHub or similar platforms
* Experience with large-scale or distributed training
* Hands-on experience with diffusion-based, transformer-based, or hybrid video generation models