Leads end-to-end QA for web and mobile applications, including test planning, execution, reporting, performance testing, security testing, defect management, and release quality assurance. Implements test automation and CI/CD integration while collaborating with product managers, developers, and international teams. The role also drives QA initiatives, investigates vulnerabilities and bottlenecks, and improves quality processes in a fast-paced startup environment.

Research and develop novel AI models, algorithms, and systems; validate them through rigorous experiments and large-scale distributed training; and transition prototypes into production. The role involves deep learning, LLM pre-training and alignment, efficient GPU/TPU utilization, scientific analysis, technical writing, mentoring, code and paper reviews, open-source contributions, and end-to-end project ownership.

Design, deploy, and scale production AI systems and applications. Responsibilities include building data pipelines, tuning and hardening models, collaborating with researchers, engineers, and product managers, monitoring live models, tracking AI advances, conducting code reviews, and owning solutions end to end amid changing priorities.