Apply low-bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality.
Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs.
Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance.
Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings.
Research and apply mixed-precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy–performance balance.
Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures.
Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication.
Author technical papers and publish findings in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI.

A degree in Computer Science or related field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
Experience with PyTorch deep learning frameworks or equivalent frameworks.
Hands-on experience with model quantization including both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ).
Research and hands-on experience with knowledge distillation for compressing large models into smaller, efficient ones.
Research and hands-on experience with model pruning for compressing large models into smaller, efficient ones.
Solid understanding of neural network architectures and training processes – Including transformers (e.g., LLMs, VLMs), backpropagation, optimization, and fine-tuning techniques.
Familiarity with C++ is a plus (especially for implementing low-level quantization kernels or inference optimizations).

AI Research Engineer – Model Compression, Quantization

Key skills