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

• 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.

• 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 ambiguous research problems, formulate hypotheses, and iterate quickly based on experimental evidence.
• Strong analytical thinking and a research-driven mindset, with an emphasis on reproducibility, scientific rigor, and measurable improvements.

• Experience with RLHF, RLAIF, preference optimization, or reinforcement learning for reasoning and agentic tasks.
• Familiarity with distributed training, large-scale experimentation, and GPU-based model optimization.
• Experience developing reward models, verifiers, graders, or automated evaluation systems.
• Research contributions to post-training, reinforcement learning, or related areas through publications, open-source projects, or production systems.
• Experience investigating synthetic data generation, curriculum learning, or iterative self-improvement techniques.

RequiredAlso useful

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