ABOUT US
We are a well-funded, early-stage AI research and product lab focused on building the next generation of frontier multimodal AI models. Founded by former DeepMind researchers - including Andrew Dai, previously an engineering leader on Gemini - our mission is to push the frontier of visual and multimodal intelligence. Our first model, Glimpse, launched publicly in October 2026.
Our team currently consists of 25 world-class scientists and engineers. We raised a $55M seed round led by Altimeter Capital, Menlo Ventures, Striker VC, and NVIDIA. We are tackling foundational problems in artificial intelligence and translating cutting-edge multimodal research into scalable, real-world systems.
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.