About the Job:
Join the company and shape the future of digital finance. As a member of our AI research team, you will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). Your work will focus on reducing model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices.
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You will apply and advance compression techniques such as quantization, knowledge distillation, and pruning to streamline complex multimodal architectures that integrate text, images, and audio. We expect you to have deep expertise in model compression methods and a strong background in multimodal model architectures.
Your responsibilities will include:
- Applying low-bit quantization to reduce model size and inference latency for generative AI models while maintaining accuracy and output quality.
- Leveraging knowledge distillation to transfer capabilities from larger teacher models to smaller student models for efficient multimodal reasoning.
- Implementing pruning techniques to remove redundant parameters, reducing computational overhead without sacrificing task performance.
- Analyzing trade-offs between model efficiency and accuracy across various methods; proposing improvements based on empirical findings.
- Researching and applying advanced compression strategies to optimize the accuracy-performance balance.
- Staying current with the latest research in model compression.
- Documenting methodologies, experiments, and results clearly to support reproducibility and communication.
- Authoring technical papers and publishing findings in top-tier conferences.
Location: United Arab Emirates (Remote)
Work Conditions: Full-time, Remote
Language Requirements: Excellent English communication skills are required.
Qualifications:
- Degree in Computer Science or related field (PhD in NLP, Machine Learning, or a related field is ideal).
- Solid track record in AI R&D with publications in A* conferences.
- Experience with PyTorch deep learning frameworks or equivalent.
- Hands-on experience with model quantization, knowledge distillation, and model pruning.
- Solid understanding of neural network architectures and training processes, including transformers.
- Familiarity with C++ is a plus.
