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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.