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We're looking for experienced AI infrastructure Engineers to design and implement robust, scalable pipelines for massive data workloads. As part of Tether’s applied research team, you’ll contribute to high-impact projects that utilize thousands of GPUs to 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 to efficiently extract, synchronize, and normalize temporal data.
- Develop automated acquisition pipelines for sourcing large-scale video datasets, managing various 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 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 for 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.
Qualifications:
- Proficient in Python with strong programming skills in backend, infrastructure, and data tooling domains.
- Strong software engineering experience, with 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 building pipelines and infrastructure with visual and multimodal datasets, including image/video pipelines.
- Experience in constructing video foundation infrastructure pipelines and workflows in collaboration with LLM and video foundation research and engineering teams is a significant advantage.
Language Requirements:
- Excellent English communication skills are essential.
