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Tether Operations

Issues fiat-collateralized stablecoins on blockchains

AI Research Engineer - Agentic Post-training

Natural Language Processing & LLMsUpdated on Nov 13
Full-timeNo salary listed

About the Job

Requirements

  • Deep expertise in large language model architectures.
  • Substantial experience in post-training for agentic workflows, including tool-use fine-tuning, function calling, and reinforcement learning from feedback on multi-turn interactions.
  • Excellent English communication skills.

Responsibilities

  • Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve state-of-the-art results.
  • Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool and 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 by incorporating 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.
  • Liaise with research, engineering, and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.
  • Refine pre-trained models to improve intelligence and domain-specific capabilities and enable reasoning, planning, and autonomous external-tool use for real-world multi-step tasks and applications on edge devices such as smartphones.
  • Work on systems ranging from resource-efficient agents for limited hardware to multimodal architectures integrating text, images, and audio, optimized for tool-augmented decision-making.
  • Develop, test, and implement post-training algorithms for goal-directed behavior, self-correction, and reliable tool invocation.
  • Curate agentic training data, including tool-use trajectories, reasoning chains, and environment interactions.
  • Strengthen baseline performance and identify and resolve bottlenecks in post-training for tool-augmented agents to improve model quality.

About the Company

Tether Operations

Issues fiat-collateralized stablecoins on blockchains

Tether Operations issues stablecoins, led by USDT, to bridge fiat currency with the crypto world. USDT is pegged to the U.S. dollar and issued on multiple blockchains (such as Ethereum, Tron, and Solana) to enable fast, low-friction transfers between exchanges. The tokens are backed by reserves and supported by periodic attestations, with revenue from reserve interest and token-related fees. The goal is to provide stable, liquid digital dollars for crypto trading and cross-border liquidity, while differentiating itself with broad blockchain support and a focus on transparency.

Company size
201-500
Company stage
IPO
Headquarters
San Salvador, El Salvador
Founded
2014

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