AI Research Resident

We are interested in research that makes AI systems more capable, reliable, efficient, and useful in the real world.

The residency program is primarily focused on the following areas:

Tool use, planning, memory, computer control, multi-agent systems, and safe execution in real-world environments.

Long-context reasoning, workflow understanding, state tracking, memory systems, and methods for maintaining coherence across complex tasks.

Capability evaluations, alignment, oversight, interpretability, robustness, red-teaming, and benchmarks for real-world task completion.

Language model training, reinforcement learning, reasoning methods, optimisation, architectures, and new approaches to improving model behaviour.

Data curation, filtering, synthetic data, mixture design, quality verification, pruning, and principled approaches to training signal.

Vision-language models, grounding, perception, multimodal reasoning, and systems that combine language, visual context, and action.