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Drive end-to-end research and engineering on vision-language and multimodal models: data curation, training pipelines, post-training workflows (fine-tuning, distillation, RLHF), efficiency and deployment optimizations, distributed GPU training, evaluation frameworks, and publishing research.

Support daily operations of a multi-asset investment and treasury platform: manage trade confirmations, settlements, reconciliations, wallet/custodian workflows, resolve breaks, maintain controls, produce reporting, support audits, and build automation and operational documentation.

As an AI Research Engineer, you will focus on model compression techniques to enhance performance of AI systems, ensuring efficient deployment on edge devices while maintaining accuracy.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you will optimize AI model serving and inference architectures, enhancing performance across various environments, focusing on efficiency and scalability.

Drive innovation in model compression and efficient deployment for multimodal AI systems. Focus on reducing model footprint and enhancing performance on resource-constrained edge devices.

The AI Research Engineer will innovate in model compression for multimodal AI systems, focusing on reducing model size and computational cost while ensuring high performance across edge devices.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will innovate in model compression techniques for large multimodal AI systems, focusing on efficiency and model deployment.

Join Tether as an AI Research Engineer focusing on model compression and quantization for AI systems. You'll innovate in reducing model footprint for multimodal architectures while applying advanced compression techniques, ensuring high performance on edge devices.