AI Video Research Engineer Intern

Join Tether and Shape the Future of Digital Finance

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting‑edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve‑backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Tether Finance: Our innovative product suite features the world’s most trusted stablecoin,
USDT
, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.

Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco‑friendly practices in state‑of‑the‑art, geo‑diverse facilities.

Tether Data: Fueling breakthroughs in AI and peer‑to‑peer technology, we reduce infrastructure costs and enhance global communications with cutting‑edge solutions like KEET
, our flagship app that redefines secure and private data sharing.

Tether

Education:

Democratizing access to top‑tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.

Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

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.

• 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

• 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

• Demonstrated related work, such as research codebase or benchmarks released on Git Hub or similar platforms
• Experience with large‑scale or distributed training
• Hands‑on experience with diffusion‑based, transformer‑based, or hybrid video generation models