Research Scientist – Multimodal Foundation Models & Visual Reasoning

Elorian • Palo Alto, CA

12h

About The Position

The Research Scientist will lead ambitious research across next-generation model architectures, distributed training methodologies, multimodal visual reasoning, reinforcement learning, and data-centric learning. With dedicated large-scale compute clusters and engineering support, the role involves formulating first-principles hypotheses and conducting large-scale empirical experiments. The scientist will collaborate with research and engineering colleagues to advance visual intelligence and translate research breakthroughs into state-of-the-art foundation models and real-world capabilities.

The work includes developing novel research in foundation models, multimodal understanding, complex visual reasoning, and spatial intelligence; creating model architectures, pre-training methodologies, and scalable post-training techniques; running distributed training experiments; advancing multimodal data curation and synthetic data generation; and partnering across research and systems engineering to deliver robust, scalable model capabilities.

Requirements

• 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 and multimodal architectures, and modern large-scale training dynamics.
• Deep proficiency in Python and modern deep learning frameworks such as PyTorch or JAX.
• Demonstrated ability to independently define research agendas, design rigorous empirical experiments, and solve open-ended, frontier-level problems.

Nice To Haves

• Deep theoretical and practical expertise in at least one of foundation models, multimodal reasoning, visual or spatial intelligence, reinforcement learning, or post-training.
• Strong research track record, demonstrated through first-author publications at top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, or ECCV, or key technical contributions to high-profile foundation model releases.
• Hands-on experience training, profiling, and scaling models across large distributed GPU or TPU clusters, using tools such as FSDP, Megatron-LM, DeepSpeed, or Ray.
• Experience developing large-scale multimodal data pipelines, generating synthetic reasoning traces, or designing rigorous evaluation suites for frontier models.

Responsibilities

• Drive research in foundation models, multimodal understanding, complex visual reasoning, and spatial intelligence.
• Conceive and build novel model architectures, pre-training methodologies, and scalable post-training techniques, including preference tuning and reasoning-focused reinforcement learning.
• Design, execute, and analyze large-scale distributed training experiments on modern accelerator clusters.
• Develop approaches to multimodal data curation, synthetic data generation, automated filtering, and benchmark evaluation.
• Collaborate with research and systems engineering teams to turn algorithmic breakthroughs into robust, scalable foundation model capabilities deployed at scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree