AI RESEARCH SCIENTIST

Architecture R&D Division | Digital Innovation Unit

Full-time | Confidential client (50,000-person global engineering group)

A 50,000-person engineering organisation is building out a dedicated Architecture R&D capability and is hiring an AI Research Scientist to keep the team at the frontier of applied AI. This is a senior individual contributor role for someone who can scan the global research landscape, run rigorous experiments, and turn emerging methods into credible proof-of-concepts that can become real products.

What you’ll do

• Track and synthesise the latest AI research across top conferences and labs (NeurIPS, ICLR, CVPR, ACADIA and beyond)
• Define research questions with the Head of AI R&D and senior architects
• Design and run structured experiments with measurable outcomes (benchmarks, ablations, comparisons)
• Build proof-of-concepts that demonstrate value in architectural / built-environment contexts
• Write clear research reports and present findings to both technical and non-technical stakeholders
• Partner closely with an ML Engineer to transition successful POCs into production-ready systems
• Evaluate ethical implications and advocate for responsible AI use
• Where appropriate: publish, contribute to conferences, and represent the organisation externally

What we’re looking for

• 5–10 years in AI research or applied science (industry lab, applied research team, or equivalent)
• Strong theoretical + practical grounding in ML, deep learning, and generative AI
• Proven ability to design experiments and interpret results with scientific rigour
• Strong Python skills; comfortable reading, adapting, and extending research code
• Excellent written communication: you can explain complex ideas clearly to mixed audiences
• Track record of translating research into practical applications
• PhD or Master’s in CS/AI (or equivalent research experience with evidence to match)

Nice to have

• Publications in AI, computational design, or related fields
• Familiarity with spatial reasoning, 3D generative models, or multimodal AI
• Exposure to architecture, urban planning, or built-environment problems
• Reinforcement learning for optimisation, simulation, or design exploration

Why this role

• High-impact research with a direct path from paper → experiment → POC → production
• Work with a serious R&D mandate inside a large-scale engineering environment
• Influence the research agenda in a fast-moving, design-adjacent AI domain