Senior AI Engineer

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?

About the job

We're seeking experienced AI infrastructure Engineers to design and implement robust, scalable pipelines for massive data workloads. Join Tether s applied research team, where you ll contribute to high-impact projects that run across thousands of GPUs and drive cutting-edge video generation foundation development.

Responsibilities

• Build and scale high-throughput data infrastructure optimized for video and multimodal content processing across large GPU clusters (e.g., H100/H200).

• Design core preprocessing algorithms for video, audio, text, and image modalities, enabling efficient extraction, synchronization, and normalization of temporal data.

• Build automated acquisition pipelines for sourcing large-scale video datasets, handling diverse formats, frame rates, annotations, and embedded audio.

• Architect robust systems for scalable evaluation and annotation, including prompt-based scoring, perceptual metrics, caption generation, and retrieval-based diagnostics.

• Collaborate with model researchers to co-design video model architectures (e.g. DiTs, VAEs, spatio-temporal transformers) and training schedules across pretraining and fine-tuning stages.

• Optimize distributed data loading and pipeline throughput for training at scale, ensuring robustness across model variants and modality combinations.

• Manage infrastructure to support experiment tracking, model versioning, and cross-team deployment workflows, integrating with production and research platforms.

• Support backend engineering across research, product, and creative teams to ensure seamless integration of data and model workflows from prototyping to inference.

• Proficient in Python with strong programming skills across backend, infrastructure, and data tooling domains.

• Strong software engineering experience, including 2+ years working with petabyte-scale data pipelines and systems across thousands of GPUs.

• Proven ability to architect and maintain large-scale distributed systems for data processing and delivery.

• Deep expertise in orchestration frameworks such as Kubernetes and SLURM with hands-on experience deploying and managing high-throughput workloads.

Preferred Qualifications

• Practical experience on building pipelines and infrastructure with visual and multimodal datasets, including image/video pipelines.

• Experience in building video foundation infrastructure pipelines and workflows with collaboration of LLM and/or video foundation research and engineering teams is a strong advantage.