AI Research Engineer (Model Compression & Quantization)

About the role

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.

What you'll do

• 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
• 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
• Research and apply mixed-precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules

What you need

• Research
• LLMs
• Deep learning
• PyTorch
• Lean
• PhD or B.Tech or B.E. or B.Sc or BCA

Good to have

• Machine learning
• NLP
• C++