We're looking for an experienced Research Scientist to advance the capabilities of large-scale vision-language and multimodal foundation models.
You'll work on challenging research problems spanning model training, post-training, reinforcement learning, and multimodal reasoning. This is a hands-on role for someone who enjoys experimenting with cutting-edge approaches, improving model performance, and translating research breakthroughs into real-world capabilities.
You'll have the opportunity to work across the model development lifecycle, from data and training strategies to evaluation and optimization, helping push the boundaries of what multimodal AI systems can understand and accomplish.
What You'll Do- Multimodal Model Research: Research, develop, and improve vision-language models and multimodal foundation-model architectures.
- Post-Training & Reinforcement Learning: Design and implement advanced training strategies, including supervised fine-tuning, preference optimization, and reinforcement learning, to improve model capabilities.
- Model Performance & Optimization: Investigate approaches to improve reasoning, generalization, reliability, and training efficiency.
- Data Quality & Training: Develop strategies for data curation, filtering, and synthetic data generation to improve training outcomes.
- Evaluation & Benchmarking: Design experiments, evaluation frameworks, and benchmarks to measure model performance, identify failure modes, and guide research priorities.
- Large-Scale Experimentation: Work with distributed training infrastructure and large-scale datasets to support research and model development.
- Research Innovation: Explore emerging approaches in multimodal learning, model alignment, and foundation-model training.
What You BringMust Have- Hands-on experience researching, training, fine-tuning, or post-training large-scale foundation models, particularly vision-language or multimodal models.
- Experience working with Qwen, Qwen-VL, or comparable open-weight multimodal foundation models.
- Strong understanding of deep learning architectures, optimization techniques, and modern model training methodologies.
- Practical experience with supervised fine-tuning (SFT), preference optimization, or reinforcement learning approaches such as DPO, GRPO, PPO, or RLHF.
- Strong proficiency in Python and PyTorch, JAX, or comparable deep learning frameworks.
- Experience designing, executing, and analyzing large-scale machine learning experiments.
- Familiarity with distributed training systems and GPU-based model development.
- Strong research instincts, analytical problem-solving skills, and the ability to independently drive complex technical projects.
- Research publications, open-source contributions, or experience within leading AI research organizations.
Nice to Have- Experience with large-scale model post-training and reinforcement learning for reasoning or alignment.
- Familiarity with multimodal data pipelines involving images, video, or other sensory inputs.
- Experience with reward modeling, synthetic data generation, or advanced model evaluation.
- Experience improving model capabilities through innovations in training strategies, data quality, or model architecture.
A Note on ApplyingStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.
