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About the job

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.

Job requirements

• 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.