AI Research Engineer

2 weeks ago

Dublin, Dublin City, Ireland Tether Operations Limited Full time

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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 solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. Using blockchain technology, Tether enables instant, secure, and global digital token transactions at low cost. Transparency ensures trust in every transaction.

Innovate with Tether

Tether Finance: Offers the trusted stablecoin USDT and digital asset tokenization services.

And more:

Tether Power: Optimizes excess power for eco-friendly Bitcoin mining in diverse facilities.

Tether Data: Advances AI and P2P tech with solutions like KEET, our private data sharing app.

Tether Education: Provides digital learning access for individuals in the digital and gig economies.

Tether Evolution: Merges technology and human potential to push innovation boundaries.

Why Join Us?

Work remotely with a global team passionate about fintech innovation. Collaborate with top talent, push boundaries, and set industry standards. If you excel in English communication and want to contribute to a leading platform, Tether is your place.

Are you ready to be part of the future?

About the job:

As part of the AI model team, you will develop architectures for models of various scales, enhancing AI capabilities and efficiency through research-driven techniques, data curation, and resolving pre-training bottlenecks.

Responsibilities:

  • Pre-train AI models on large, distributed NVIDIA GPU servers.
  • Design and prototype innovative architectures.
  • Conduct experiments, analyze results, and refine methods.
  • Debug and improve model efficiency and performance.
  • Advance training systems for scalability and efficiency.

Require a degree in Computer Science or related field, preferably a PhD in NLP, ML, or similar, with a strong research record. Hands-on experience with large-scale LLM training, distributed frameworks, and deep knowledge of transformer models and PyTorch/Hugging Face libraries is essential.

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