AI Research Engineer Agentic Posttraining 100% Remote Worldwide

About Us

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.

Our innovative product suite features the world's most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. Beyond finance, we drive sustainable growth through energy solutions that optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. We fuel breakthroughs in AI and peer-to-peer technology with Tether Data, reducing infrastructure costs and enhancing global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing. We're democratizing access to top-tier digital learning through Tether Education, empowering individuals to thrive in the digital and gig economies. At Tether Evolution, we're pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Our team is a global talent powerhouse, working remotely from every corner of the world. We've grown fast, stayed lean, and secured our place as a leader in the industry.

The Role

As a member of the AI model team, you will drive innovation in post-training methodologies, with a special focus on agentic behaviors and tool use. Your work will refine pre-trained models so that they not only deliver enhanced intelligence and domain-specific capabilities, but also learn to reason, plan, and autonomously invoke external tools to solve real-world, multi-step tasks and applications on edge devices (i.e., smartphones).

You will work on a wide spectrum of systems, ranging from streamlined, resource-efficient agents that run on limited hardware to complex multi-modal architectures integrating text, images, and audio, all optimized for tool-augmented decision making.

We expect you to have deep expertise in large language model architectures and substantial experience in post-training for agentic workflows, including tool use fine-tuning, function calling, and reinforcement learning from feedback on multi-turn interactions. You will adopt a hands-on, research-driven approach to developing, testing, and implementing new post-training algorithms that unlock goal-directed behavior, self-correction, and reliable tool invocation.

Your responsibilities include curating agentic training data (e.g., trajectories of tool use, reasoning chains, environment interactions), strengthening baseline performance, and identifying as well as resolving bottlenecks in post-training for tool-augmented agents to achieve state-of-the-art model quality. The goal is to build models that do not just know but also act, use tools, and adapt, pushing the limits of what agentic AI can achieve.

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/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 state-of-the-art 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).