Research Scientist - Multimodal Foundation Models & Visual Reasoning

About the Role

In this role, you will lead ambitious, high-impact research across next-generation model architectures, distributed training methodologies, multimodal visual reasoning, reinforcement learning, and data-centric learning. Backed by dedicated, large-scale compute clusters and deep engineering support, you will formulate first-principles hypotheses and run large-scale empirical experiments.

You will collaborate closely with world-class research and engineering peers to push the boundary of visual intelligence and translate core research breakthroughs directly into state-of-the-art foundation models and real-world capabilities.

What You Will Do

• Frontier Research: Drive novel research at the intersection of foundation models, multimodal understanding, complex visual reasoning, and spatial intelligence.
• Architecture & Training Innovations: Conceive and build novel model architectures, pre-training methodologies, and scalable post-training techniques (e.g., preference tuning, reasoning-focused RL) to systematically advance model capabilities.
• Large-Scale Experimentation: Design, execute, and analyze large-scale distributed training experiments on modern accelerator clusters to push the frontier of model performance.
• Data-Centric Intelligence: Pioneer innovative approaches to multimodal data curation, synthetic data generation, automated filtering, and rigorous benchmark evaluation.
• Breakthrough to Capability: Collaborate with cross-functional research and systems engineering teams to turn fundamental algorithmic breakthroughs into robust, scalable foundation model capabilities deployed at scale.

What We Are Looking for

• Ph.D. or equivalent industry research track record in Machine Learning, Computer Vision, Natural Language Processing, or a related quantitative discipline.
• Rigorous foundational understanding of deep learning principles, transformer/multimodal architectures, and modern large-scale training dynamics.
• Deep proficiency in Python and modern deep learning frameworks (PyTorch or JAX).
• Demonstrated ability to independently define research agendas, design rigorous empirical experiments, and solve open-ended, frontier-level problems.

Nice to Have

• Specialized Domain Expertise: Deep theoretical and practical expertise in at least one of the following: foundation models, multimodal reasoning, visual/spatial intelligence, reinforcement learning, or post-training
• Proven Research Impact: Strong track record demonstrated through first-author publications at top-tier venues (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) or key technical contributions to high-profile foundation model releases.
• Distributed Systems & Scale: Hands-on experience training, profiling, and scaling models across large distributed GPU or TPU clusters (e.g., FSDP, Megatron-LM, DeepSpeed, Ray).
• Data & Evaluation: Demonstrated experience developing large-scale multimodal data pipelines, synthetic reasoning trace generation, or designing rigorous evaluation suites for frontier models.