AI Research Engineer (Multi-Modal Reinforcement Learning) - 100% Remote Worldwide

***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.

***Innovate with Tether***

**Tether Finance:** 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.

But that’s just the beginning:

**Tether Power:** Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.

**Tether Data:** Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like **KEET**, our flagship app that redefines secure and private data sharing.

**Tether Education**: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.

**Tether Evolution**: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

***Why Join Us?***

Our team is a global talent powerhouse, working remotely from every corner of the world.

If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards.

We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

Are you ready to be part of the future?

As a member of the AI model team, you will drive innovation in multi-modal reinforcement learning to advance next-generation intelligent systems.

Your work will focus on optimizing decision-making and adaptive behavior across integrated data modalities such as text, images and audio to deliver enhanced intelligence, robust performance, and domain-specific capabilities for real-world challenges.

You will develop and scale reinforcement learning techniques within complex multi-modal architectures, including diffusion-based generative models and autoregressive models for multimodal understanding, as well as resource-efficient models designed for constrained hardware environments.

This includes conducting research on reinforcement learning algorithms for multimodal models, spanning diffusion models for image autoregressive models for multimodal reasoning, and unified multimodal frameworks.

You are expected to have deep expertise in designing multi-modal reinforcement learning systems and a strong background in advanced model architectures, with a hands-on, research-driven approach to building and deploying novel algorithms and training frameworks.

You will design and develop RL infrastructure and reward modeling strategies to enable efficient large-scale training, improve training stability, and mitigate reward hacking and related failure modes.

Your responsibilities also include curating multi-modal simulation environments and training datasets, improving baseline policy performance across modalities, and identifying and resolving bottlenecks in multi-modal learning and reward optimization.

In addition, you will explore next-generation reinforcement learning paradigms that more directly and effectively learn from environment feedback, with the goal of unlocking superior, domain-adapted AI performance in dynamic, real-world environments.