Remote AI Research Engineer - Model Compression & Multimodal

CryptoproNetwork; Abu Dhabi; Remote; AED 300,000 - 420,000; Full time

Job summary

Tether Operations Limited is hiring an AI Research Engineer to lead model compression and efficient deployment for multimodal AI systems. Responsibilities include reducing footprint and latency on edge devices while preserving accuracy, using quantization, distillation and pruning across text, image, and audio modalities.

The role emphasizes hands-on research, rigorous evaluation, and clear documentation to support reproducibility and collaboration across teams in a globally distributed

Qualifications

• Deep expertise in model compression methods and multimodal architectures.
• Hands-on, research-driven approach to develop, test and implement compression strategies.
• Experience with balancing model size, latency, throughput and accuracy.

Responsibilities

• Apply low-bit quantization to reduce model size and latency while preserving accuracy.
• Use knowledge distillation to transfer capabilities from larger to smaller models for multimodal inputs.
• Implement pruning to remove redundant parameters and attention heads.
• Analyze trade-offs between efficiency and accuracy across compression methods and propose improvements.
• Stay current with new compression techniques for multimodal and generative models.
• Document methodologies, experiments and results for reproducibility and collaboration.
• Publish findings in top-tier conferences to advance the field.

Skills

Model compression

Quantization

Knowledge distillation

Pruning

Multimodal AI

Edge deployment

Research methodology

Academic publishing

Job description

Tether Operations Limited is hiring an AI Research Engineer to lead model compression and efficient deployment for multimodal AI systems. Responsibilities include reducing footprint and latency on edge devices while preserving accuracy, using quantization, distillation and pruning across text, image, and audio modalities.

The role emphasizes hands-on research, rigorous evaluation, and clear documentation to support reproducibility and collaboration across teams in a globally distributed