Description
The Applied Scientist will develop high-performance Generative AI features across Cloud and Edge environments, focusing on local inference optimization through model compression and quantization, scalable RAG architectures, and multi-agent systems. The role covers the full research lifecycle, including hypothesis formulation, synthetic-data generation, LLM fine-tuning, safety and alignment validation, benchmarking, and technical reporting, while collaborating with Product and Data Engineering teams. The position requires at least three years of commercial machine-learning experience, strong Python and PyTorch expertise, and production deployment experience with LLM inference systems.
