AI Research Engineer (Fine-tuning - 100 Remote Argentina)

Job Title: AI Model Developer

Pioneering the Future of Digital Finance with Tether Operations Limited

We are a leading innovator in the digital finance space, pushing the boundaries of what is possible through cutting-edge solutions. Our mission is to empower businesses worldwide to seamlessly integrate reserve-backed tokens across blockchains, unlocking new opportunities for growth and innovation.

About the Job

We are seeking an exceptional AI Model Developer to join our team. As a member of our talented group, you will play a critical role in driving innovation in supervised fine-tuning methodologies for advanced models. Your expertise will refine pre-trained models, delivering enhanced intelligence, optimized performance, and domain-specific capabilities designed for real-world challenges.

Your Responsibilities:

• Develop State-of-the-Art Fine-Tuning Methodologies

You will develop and implement novel fine-tuning techniques and algorithms, focusing on optimizing model performance and domain adaptation.

• Curate Specialized Data and Strengthen Baseline Performance

You will identify and process high-quality datasets tailored to specific domains, ensuring that data curation positively impacts model performance in fine-tuning tasks.

• Debug and Optimize the Fine-Tuning Process

You will systematically debug and optimize the fine-tuning process by analyzing computational and model performance metrics, enabling continuous improvement and refinement.

• Collaborate with Cross-Functional Teams

You will collaborate with cross-functional teams to deploy fine-tuned models into production pipelines, defining clear success metrics and ensuring continuous monitoring for improvements and domain adaptation.

Requirements:

• A degree in Computer Science or related field. Ideally 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.
• Deep understanding of advanced fine-tuning methodologies, including state-of-the-art modifications for transformer architectures as well as alternative approaches.
• 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.
• Demonstrated ability to apply empirical research to overcome fine-tuning bottlenecks, designing evaluation frameworks and iterating on algorithmic improvements to continuously push the boundaries of fine-tuned AI performance.