Member of Technical Staff, Model Training (post-training research)

Job Title: Member of Technical Staff, Model Training (post-training research) Job Type: Full-time Location : Remote The Role In this role, you will investigate how learning algorithms, training data, and evaluation methodologies interact to produce stronger reasoning, generalization, and agent capabilities. You will explore new approaches to reinforcement learning, distillation, and data-efficient training, translating research insights into scalable methods that improve model intelligence. This is a highly experimental role at the intersection of fundamental machine learning research and large-scale model development. You will have the opportunity to formulate research hypotheses, design ambitious experiments, and develop training methods that directly influence the capabilities of our models. What You'll Do Research, implement, and scale advanced post-training methods, including supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), across large language and multimodal models. Investigate learning objectives, optimization dynamics, exploration strategies, and credit assignment mechanisms to improve training stability, sample efficiency, and capability acquisition. Develop and refine training recipes by jointly optimizing data mixtures, sampling strategies, curricula, and evaluation methodologies. Design methods for learning from expert demonstrations, human preferences, corrections, and model-generated trajectories, identifying where human feedback delivers the greatest improvements. Research the co-evolution of generators, reward models, and graders, developing approaches that improve both model performance and the quality of the training signals they consume. Design rigorous experiments, ablation studies, and evaluation frameworks to measure generalization, reasoning, and performance on realistic downstream tasks. Own research initiatives end-to-end, from identifying promising directions and implementing new algorithms to executing large-scale experiments and validating results. What We're Looking For Strong foundations in machine learning, deep learning, optimization, and modern model training techniques. Hands-on experience training, fine-tuning, or post-training large-scale language or multimodal models. Deep technical expertise in one or more areas such as reinforcement learning, model distillation, reward modeling, data selection, or learning dynamics. Strong proficiency in Python and PyTorch, with the ability to implement, debug, and optimize complex training pipelines. Experience designing controlled experiments, interpreting model behavior, and translating empirical findings into actionable research directions. Ability to independently navigate ambiguo