Conduct original research in core machine learning areas including deep learning, representation learning, generative modeling, optimization, and large-scale model architectures Design, implement, and evaluate novel ML algorithms and models using frameworks such as PyTorch, iterating based on experimental results and data-driven insights Run controlled experiments to test research hypotheses, analyze outcomes, and make informed decisions in collaboration with research and engineering partners Write clean, well-structured research code and contribute to shared codebases, ensuring reproducibility and maintainability of experiments Produce and submit research findings to top-tier machine learning conferences and workshops such as NeurIPS, ICML, ICLR, or similar venues Collaborate with cross-functional partners across research, engineering, and product teams to integrate research innovations into production systems at scale Participate in code and design reviews, providing and incorporating technical feedback to improve research quality and implementation robustness Build logging, monitoring, and evaluation pipelines to track model performance and detect regressions during research and deployment phases Contribute to the health of the research community by participating in programs such as reading groups, research reviews, and onboarding support for incoming researchers