AI Research Engineer – Agentic Post-training

🕒 May 19

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Tether.to

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results. • Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration. • Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure. • Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment. • Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training. • Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models. • Closely liaise with research, engineering and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.

🎯 Requirements

• Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) preferred with a strong publication record in top-tier AI conferences. • Experience with multimodal post-training workflows and data pipelines, particularly for agentic systems and tool use. • Hands-on experience applying post-training at scale using distributed training frameworks (e.g., multi-node GPU environments). • Demonstrated experience improving model capabilities in areas such as reasoning, tool use, and multi-agent coordination that achieve SOTA results. • Proven track record of open-source contributions related to agentic systems or tool use (code, datasets, or models) on platforms such as GitHub or Hugging Face. • Publications at leading AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, ECCV).

🏖️ Benefits

• Flexible work arrangements • Professional development opportunities