AI Research Engineer (Fine-tuning)

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 enable seamless integration of reserve-backed tokens across blockchains, empowering businesses worldwide with secure, instant, and cost-effective digital transactions. Transparency and trust are fundamental to our mission.

Innovate with Tether

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

Tether Power : We promote sustainable Bitcoin mining using eco-friendly practices in geo-diverse facilities.

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

Tether Education : We democratize digital learning to empower individuals in the digital economy.

Tether Evolution : We innovate at the intersection of technology and human potential to shape the future.

Why Join Us?

Our diverse, remote team is passionate about fintech innovation. Join us to work with top talent, set new industry standards, and contribute to a groundbreaking platform. Excellent English communication skills are essential.

Are you ready to be part of the future?

About the job :

As part of the AI model team, you will develop and refine supervised fine-tuning methodologies for advanced models, enhancing their intelligence, performance, and domain-specific capabilities across various systems, from lightweight models to complex multi-modal architectures.

We seek expertise in large language model architectures, fine-tuning optimization, and a hands-on, research-driven approach. Your responsibilities include curating data, improving baseline performance, troubleshooting bottlenecks, and deploying models into production.

Responsibilities :

Develop and implement innovative fine-tuning methodologies with clear performance goals.

Design, execute, and monitor fine-tuning experiments, documenting results and benchmarking.

Curate high-quality, domain-specific datasets and define measurable criteria for data impact.

Analyze performance metrics to debug and optimize the fine-tuning process.

Collaborate with teams to deploy models, define success metrics, and monitor ongoing performance.

Minimum requirements :

A degree in Computer Science or related field; PhD in NLP, Machine Learning, or similar is preferred, with a strong publication record.

Proven experience in large-scale fine-tuning experiments with measurable improvements.

Expertise in transformer architectures, fine-tuning methodologies, and model efficiency.

Strong skills in PyTorch and Hugging Face, with practical deployment experience.

Ability to translate empirical research into scalable solutions and evaluate algorithmic improvements.

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