AI Research Resident

Maincode is an Australian AI research company building Matilda, an assistant that understands complex work, reasons over context, and takes meaningful action safely. The AI Research Residency is a paid 3 to 6 month program for late-stage PhD students and exceptional early-career researchers who want to pursue high-impact AI research grounded in real systems. Residents work closely with Maincode's research and engineering teams, with dedicated access to large-scale GPU compute and our in-house research infrastructure. You will explore open problems, run experiments at scale, and produce work that can contribute to top-tier publications, open research, infrastructure, or the systems behind Matilda. This role is research-first, but applied. Strong projects may take the form of model research, evaluations, infrastructure, technical engineering work, or product-facing research that improves Matilda and future Maincode systems. Research areas 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: Agents • 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. Safety and evaluation • Capability evaluations, alignment, oversight, interpretability, robustness, red-teaming, and benchmarks for real-world task completion. Training and algorithms • Language model training, reinforcement learning, reasoning methods, optimisation, architectures, and new approaches to improving model behaviour. Data • Data curation, filtering, synthetic data, mixture design, quality verification, pruning, and principled approaches to training signal. Multimodal systems • Vision-language models, grounding, perception, multimodal reasoning, and systems that combine language, visual context, and action. Efficiency and infrastructure • Training and inference efficiency, kernels, parallelism, sharding, decoding, quantisation, scheduling, and systems for running large models reliably. Responsibilities • Lead research that advances Maincode's work on capable, useful, and trustworthy AI systems. • Design and execute experiments, develop new research directions, and collaborate closely with our researchers and engineers. • Produce research outputs suitable for top-tier conferences, journals, technical reports, open-source releases, or deployment in Matilda and future Maincode systems. Qualifications • Late-stage PhD student, recent PhD graduate, or exceptional early-career researcher. • Strong research taste and the ability to identify important problems before they are obvious. • Experience publishing, preprinting, or producing high-quality research in AI, machine learning, or adjacent technical fields. • Ability to define and execute independent research under uncertainty. • Interest in building AI systems that can reason, act, and operate reliably in complex real-world environments. What you will have access to • Dedicated access to Maincode's GPU compute and research infrastructure. • Close collaboration with a small, high-calibre team across AI research, systems engineering, product engineering, and design. • Support for top-tier conference and journal submissions, with the opportunity for strong research to contribute to Matilda and future Maincode systems. • A research environment focused on deep work, technical seriousness, and real-world impact.