Senior AI Research Scientist (Robotics)

Location: Bristol, United Kingdom

Company

hirify.global develops general-purpose robotic systems and embodied-AI capabilities for physical robots.

What you will do

• Set and execute research programmes in robot learning, multimodal representation learning, imitation learning, reinforcement learning and foundation models for embodied intelligence.
• Develop methods for acquiring, generalising and adapting manipulation and whole-body skills from demonstrations, interaction data, simulation and deployment experience.
• Explore vision-language-action models, generative policies, hierarchical approaches and multimodal architectures connecting vision, language, action and robot state.
• Design experiments, ablations and evaluation protocols that distinguish genuine capability improvements from benchmark or demonstration effects.
• Build research prototypes and validate them with robot-learning, controls, perception and platform engineers on physical hardware.
• Investigate robustness, transfer, continual adaptation, uncertainty, failure detection, recovery and safety-aware learning for robots operating around people.

Requirements

• Strong research track record in machine learning, robot learning, computer vision, reinforcement learning, multimodal AI or a related field.
• Original research contributions demonstrated through publications, deployed methods, patents, open-source work or equivalent industrial impact.
• Deep knowledge of modern deep-learning methods and practical experience with PyTorch, large-scale experimentation and reproducible research code.
• Experience with high-dimensional sequential decision problems, multimodal data or learned control policies.
• Strong mathematical and statistical foundations, with the ability to turn capability goals into testable hypotheses and clear technical conclusions.
• PhD in machine learning, robotics, computer science or a related field, or equivalent research experience and impact.

Nice to have

• Experience learning on physical robots, particularly manipulation, bimanual systems, humanoids or contact-rich tasks.
• Experience with vision-language-action models, diffusion or flow-based policies, transformers and multimodal foundation models.
• Knowledge of offline or model-based reinforcement learning, world models, inverse reinforcement learning, human feedback and demonstrations.
• Experience with robot datasets, teleoperation, autonomous data collection, synthetic data, sim-to-real transfer, domain adaptation or continual learning.
• Experience with safety-aware learning, uncertainty estimation, out-of-distribution detection, recovery behaviour and deployment on robots.

Culture & Benefits

• Close collaboration with engineering teams across robot learning, controls, perception and platform development.
• Opportunities to communicate research through technical reports, internal talks, publications and open-source contributions.
• Scientific leadership through reviewing experimental work, challenging assumptions, mentoring colleagues and shaping the embodied-AI roadmap.
• Occasional travel to partner or pilot sites may be required.
• Reasonable accommodations may be provided for individuals with disabilities.