• Conduct applied research related to AI model evaluation, data quality, and validation methodologies • Co-author research papers, technical reports, and whitepapers • Implement research prototypes and experimental systems • Support internal R&D initiatives in areas such as Agentic AI systems, LLM evaluation and validation, Speech and multimodal model assessment, Data-centric AI methodologies • Collaborate with the engineering team to translate research ideas into practical workflows • Develop experimental code and proof-of-concept implementations • Work with datasets used for training, evaluation, and benchmarking • Design experiments and analyze results • Write technical blog articles explaining recent advances in AI and ML • Translate complex research topics into accessible technical content • Support marketing and communications teams with technically accurate material • Contribute educational materials and technical explainers • Attend academic and industry conferences • Engage with researchers from AI companies and academia • Discuss research topics, evaluation challenges, and data requirements • Identify opportunities for collaboration related to dataset needs and model evaluation • Maintain professional follow-up communication after conferences