Phd jobs in United States

Join Tether and Shape the Future of Digital Finance

At Tether, we're pioneering a global financial revolution with innovative solutions that empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. Our blockchain technology enables instant, secure, and global digital token transactions at a fraction of the cost, built on transparency and trust.

Innovate with Tether

Tether Finance: Our product suite includes the trusted stablecoin USDT, used worldwide, and pioneering digital asset tokenization services.

But that's just the beginning:

Tether Power: We drive sustainable growth by optimizing excess energy for eco-friendly Bitcoin mining in geo-diverse facilities.

Tether Data: We advance AI and peer-to-peer tech with solutions like KEET, our app for secure, private data sharing.

Tether Education: We democratize digital learning, empowering individuals in the gig economy and driving global growth.

Tether Evolution: We push technological boundaries to merge innovation with human potential, shaping a future of unprecedented possibilities.

Why Join Us?

Our global, remote team is passionate about fintech innovation. If you excel in English communication and want to contribute to a pioneering platform, Tether offers an environment to collaborate with top talent, push boundaries, and set new industry standards.

About the job:

As part of the AI model team, you'll innovate in architecture development for models of various scales, including small, large, and multi-modal systems. Your work will enhance AI capabilities, efficiency, and performance.

You should have deep expertise in LLM architectures, pre-training optimization, and a research-driven approach. Your goal is to explore and implement novel techniques, improve data curation, strengthen baselines, and resolve pre-training bottlenecks to advance AI performance.

Responsibilities:

• Conduct pre-training of AI models on large, distributed servers with thousands of NVIDIA GPUs.
• Design, prototype, and scale innovative architectures to improve model intelligence.
• Execute experiments, analyze results, and refine methodologies collaboratively.
• Debug and enhance model efficiency and computational performance.
• Advance training systems for seamless scalability and efficiency on target platforms.

Qualifications:

• Degree in Computer Science or related field; PhD in NLP, Machine Learning, or related fields preferred, with a strong record in AI R&D and publications in top conferences.
• Hands-on experience with large-scale LLM training on distributed NVIDIA GPU servers, ensuring scalability and impactful performance improvements.
• Practical knowledge of large-scale, distributed training frameworks and tools.
• Deep understanding of transformer and non-transformer modifications to enhance intelligence, efficiency, and scalability.
• Expertise in PyTorch and Hugging Face libraries, with practical experience in model development, pretraining, and deployment.