• Research and develop state-of-the-art voice synthesis models (e.g., TTS, voice cloning, speech-to-speech). • Build and fine-tune models using frameworks like PyTorch and HuggingFace. • Design training pipelines and datasets for scalable voice model training. • Explore techniques for emotional expressiveness, multilingual synthesis, and speaker adaptation. • Work closely with product and creative teams to ensure models meet quality and production constraints. • Stay on top of academic and industrial trends in speech synthesis and related fields.

• Finetune LLMs using RL and other SOTA methods • Create conversational workflows involving tool-calls and static knowledge • Work with agent teams to build business logic into conversation • Employ multimodals to enrich the conversational UX • Innovate and build novel methods for faster inference

• 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.

• Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). • Focus on reducing model footprint and computational cost while preserving accuracy. • Apply and advance compression techniques such as quantization, knowledge distillation, and pruning. • Develop, test, and implement novel compression strategies that balance model size, latency, throughput, and accuracy. • Build robust compression pipelines, establishing performance and fidelity metrics, and addressing bottlenecks in production inference.