AI Research Scientist

• 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.

AI Research Engineer – Model Compression, Quantization

• Drive innovation in model serving and inference architectures for advanced AI systems
• Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency
• Ensure pipelines run efficiently across diverse environments
• Establish clear performance targets
• Build, run, and monitor controlled inference tests
• Identify and prepare high-quality test datasets and simulation scenarios
• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline
• Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines

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AI Research Engineer – Model Compression, Quantization

AI Research Engineer – Kernel & Inference Optimization