Head of AI R&D: Tether

Tether is a global financial technology company pioneering a financial revolution through reserve-backed tokens and blockchain technology. Beyond its well-known stablecoin, USDT, Tether has divisions focused on sustainable Bitcoin mining (Tether Power), AI and peer-to-peer technology (Tether Data), and digital education.

As the Head of AI (R&D), you will lead cross-functional teams to drive innovation in training multimodal large language models (LLMs), video foundation models, and other advanced AI systems. You will play a pivotal role in shaping the company's AI strategy, from research breakthroughs to production-ready solutions, reporting directly to senior leadership.

Responsibilities
Lead and mentor interdisciplinary teams of researchers, engineers, and data scientists.

Innovate on model architectures, training methodologies, and optimization techniques for multimodal LLMs and video foundation models.

Scale the R&D team by recruiting top talent and fostering a collaborative culture.

Oversee scalable training pipelines, including data curation and distributed computing strategies.

Deliver high-quality, general-purpose models tailored to specific applications for both cloud and edge devices.

Collaborate with product and engineering teams to transition research prototypes into production systems.

Publish research findings and represent the company at conferences.

Requirements
Required Experience:

A PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

Proven experience leading AI R&D teams (5+ years in a leadership role) with a track record of delivering production-grade models.

Deep expertise in training foundation models, including multimodal LLMs and video models.

Hands-on experience with frameworks like PyTorch, TensorFlow, or JAX.

A strong understanding of scalable training techniques, such as data and model parallelism.

Preferred Experience:

Publications in top-tier conferences/journals (e.g., NeurIPS, CVPR, ICML).

Experience with real-world deployments of AI models in both datacenter and edge settings.

Familiarity with ethical AI, bias mitigation, and regulatory compliance.

Strong programming skills in Python and experience with distributed systems (e.g., Kubernetes, Ray).