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 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.

As a member of the AI model team, you will drive innovation in supervised fine-tuning methodologies for advanced models. Your work will refine pre-trained models so that they deliver enhanced intelligence, optimized performance, and domain-specific capabilities designed for real-world challenges. You will work on a wide spectrum of systems, ranging from streamlined, resource-efficient models that run on limited hardware to complex multi-modal architectures that integrate data such as text, images, and audio.

We expect you to have deep expertise in large language model architectures and substantial experience in fine-tuning optimization. You will adopt a hands-on, research-driven approach to developing, testing, and implementing new fine-tuning techniques and algorithms. Your responsibilities include curating specialized data, strengthening baseline performance, and identifying as well as resolving bottlenecks in the fine-tuning process. The goal is to unlock superior domain-adapted AI performance and push the limits of what these models can achieve.

• Develop and implement new state-of-the-art and novel fine-tuning methodologies for pre-trained models with clear performance targets.
• Build, run, and monitor controlled fine-tuning experiments while tracking key performance indicators. Document iterative results and compare against benchmark datasets.
• Identify and process high-quality datasets tailored to specific domains. Set measurable criteria to ensure that data curation positively impacts model performance in fine-tuning tasks.
• Systematically debug and optimize the fine-tuning process by analyzing computational and model performance metrics.
• Collaborate with cross-functional teams to deploy fine-tuned models into production pipelines. Define clear success metrics and ensure continuous monitoring for improvements and domain adaptation.

A degree in Computer Science or related field is required. Ideally, a PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).

Hands-on experience with large-scale fine-tuning experiments, where your contributions have led to measurable improvements in domain-specific model performance is essential. You should have a deep understanding of advanced fine-tuning methodologies, including state-of-the-art modifications for transformer architectures as well as alternative approaches. Your expertise should emphasize techniques that enhance model intelligence, efficiency, and scalability within fine-tuning workflows.

Strong expertise in PyTorch and Hugging Face libraries with practical experience in developing fine-tuning pipelines, continuously adapting models to new data, and deploying these refined models in production on target platforms is necessary. You should also demonstrate the ability to apply empirical research to overcome fine-tuning bottlenecks, be comfortable designing evaluation frameworks, and iterating on algorithmic improvements to continuously push the boundaries of fine-tuned AI performance.