Members of Technical Staff (AI)

Location: London, United Kingdom; on-site, working in person every day

Company

hirify.global is a well-funded, fast-growing frontier AI lab building AI systems that recursively self-improve and discover new scientific knowledge.

What you will do

• Develop AI Scientist agents for meaningful in silico and in situ discoveries in specific scientific domains.
• Design AI for Science research settings suitable for open-ended exploration and benchmark proprietary agents in those environments.
• Create evaluations and training data, contribute expert feedback to post-training, and improve agent performance through harness iteration.
• Build interfaces connecting agents to scientific software, simulators, cloud labs, and other experiment infrastructure.
• Drive experimental iteration on agent behaviour and close recursive loops by enabling agents to deploy, debug, and repair themselves.
• Collaborate with scientific partners and customers to validate and forward-deploy systems.

Requirements

• 5+ years of experience in hard science research, AI for Science, data science, or industry R&D.
• Experience applying machine learning to scientific progress using in silico or in situ datasets.
• A successful research track record demonstrated through papers, product releases, open-source contributions, or comparable work.
• 3+ years of software engineering experience, including Python and at least one deep learning framework.
• Experience using the latest coding agents and a clear perspective on effective workflows.
• Enthusiasm for experimental organizational design and an agent-centred approach to building companies.

Nice to have

• PhD in a scientific discipline.
• Well-cited work in AI for Science, such as protein structure prediction, neural weather forecasting, materials discovery, or causal models for financial risk.
• Hands-on experience using AI agents to automate scientific workflows in a specific domain.
• Familiarity with foundation-model post-training, harnessing, open-endedness, meta-learning, or recursive self-improvement.
• Experience with autonomous labs or strong relationships with leading scientists in a field suited to low-latency, high-throughput experiments.

Culture & Benefits

• Work at the beginning of a frontier AI lab and shape its core research direction.
• Focus on genuine scientific discovery rather than incremental benchmark improvements.
• Small, high-trust team with minimal bureaucracy and a technical culture.
• Build products intended to help leading scientists achieve breakthrough research while preserving human agency.
• Unusual career paths and diverse perspectives are welcomed.