Jobs at Tether.io

Own the strategy, roadmap, partner selection, integration, routing, and performance optimization for Tether Wallet’s fiat on-ramp and token swap experiences. Evaluate payment methods, providers, fees, conversion, compliance, and market coverage across global markets. Lead partner onboarding and collaborate with Legal, Compliance, Business Development, Engineering, Design, and expansion teams to deliver seamless USDT and XAUT purchasing. Track funnel KPIs, manage partner performance, and improve conversion and user experience.

Own Keet’s long-term vision, strategy, growth, and execution as a private peer-to-peer messaging and calling platform. Lead product and engineering teams, manage sprint delivery and cross-functional dependencies, guide P2P architecture decisions, oversee voice/video quality, rapidly prototype with AI tools, and manage mobile and desktop releases. Present strategy to executives and the board while driving the product toward one billion users.

Coordinates event-related creative requests from intake through delivery, briefing designers, managing timelines, reviewing print and digital assets, maintaining documentation, coordinating vendors, and identifying production or logistical risks. The role ensures brand, file, print, and platform specifications are met while improving event creative workflows and keeping cross-functional stakeholders aligned.

Build functional peer-to-peer applications and infrastructure with Pear and Bare, then create polished screen-share demos, code-along videos, guides, and interactive repositories. Translate cryptography, protocols, and distributed systems into accessible developer content. Collaborate with core engineering teams, technical influencers, and developer advocates to prototype applications and accelerate adoption of Tether’s decentralized technology ecosystem.

Architect and maintain GitHub-based CI/CD pipelines, test-driven deployments, Docker containerization, and automated release workflows. Manage JavaScript, TypeScript, and C++ package lifecycles; build cross-platform applications with CMake; and automate web, desktop, and mobile releases. Provision secure, scalable infrastructure using IaC tools while supporting Linux administration, networking, monitoring, troubleshooting, and release reliability across distributed teams.

Own end-to-end product design for digital wallets, from concept and prototyping through launch and iteration. Simplify complex crypto and self-custody experiences for new users, collaborate with product, engineering, research, and leadership, and raise design quality. The role also contributes to wallet strategy and requires rapid prototyping, strong visual and interaction design, and understanding of crypto networks, fees, and emerging financial users.

As a Research Engineer Intern, you'll develop state-of-the-art LLM and multimodality models, optimize distributed training frameworks, and collaborate on the application of large models in business scenarios.

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will innovate in model compression techniques for large multimodal AI systems, focusing on efficiency and model deployment.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Drive research and engineering for large-scale LLM and multi-modal pre-training: design architectures, curate massive datasets, run distributed training on multi-node GPU clusters, optimize performance, and advance cross-modal alignment and efficiency.