AI Research Engineer – Model Compression, Quantization

Responsibilities

• 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., Neur IPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI.

Qualifications

• A degree in Computer Science or related field.
• Ideally Ph D 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 Py Torch 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).
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