Research Engineer / Scientist, World Models & Robot Learning

Dynamic Creatures; Cambridge; On-site; GBP 65,000 - 100,000; Full time

Job summary

Dynamic Creatures in Cambridge is seeking a hands-on researcher or engineer to develop visual world models for robots. You will learn from images, video, and robot interaction data to build representations of the physical world and predictive models that guide robot policies.

The role emphasizes self-supervised learning, action-conditioned prediction, and close collaboration with robotics and controls engineers to apply research to real-world robots.

Qualifications

• Hands-on experience with world models or video representation learning.
• Strong Python and PyTorch skills with ownership of model training and evaluation.
• Solid foundation in computer vision and temporal/sequential modelling.
• Ability to run rigorous experiments with clear baselines and evaluations.

Responsibilities

• Build and evaluate visual world models that capture scene dynamics and object interactions.
• Develop models from video and robot data, including self-supervised learning and action-conditioned prediction.
• Connect learned representations to planning and robot behaviour in collaboration with engineers.
• Own experiments from data collection to training, evaluation and validation on physical systems.
• Design evaluations testing predictive accuracy, generalisation, robustness and usefulness for downstream tasks.
• Investigate failures and address inference latency and compute constraints.

Skills

Python

PyTorch

Computer vision

Experimentation

Temporal modelling

Tools

PyTorch

OpenCV

NumPy

Job description

Help robots understand what happens next.

Dynamic Creatures builds interactive character robots for entertainment and hospitality. We are looking for a hands-on researcher or engineer to develop visual world models that help robots understand their surroundings, anticipate how situations evolve, and use those predictions to guide behaviour.

The role centres on learning from images, video and robot interaction data. You will investigate representations of the physical world, predictive and action-conditioned models, and the connection between visual understanding and robot policies. Experience with vision-language-action models is particularly relevant.

What you will do

• Build and evaluate visual world models that capture scene dynamics, object interactions and the effects of robot actions.
• Develop models and representations from video and robot data, including self-supervised learning and action-conditioned prediction where appropriate.
• Connect learned representations and predictions to planning, policy learning and robot behaviour in collaboration with robotics and controls engineers.
• Own experiments from data collection and curation through model training, evaluation and validation on physical systems.
• Design evaluations that test predictive accuracy, generalisation, robustness and usefulness for downstream robot tasks.
• Investigate failures, improve data and models, and address inference latency and compute constraints.

What we are looking for

• Substantial hands-on experience in world models, video representation learning, predictive modelling or closely related robot-learning research.
• Strong Python and PyTorch skills, with personal ownership of model training and evaluation.
• A strong foundation in computer vision, deep learning and temporal or sequential modelling.
• Evidence of rigorous experimentation: clear baselines, meaningful evaluations, and an ability to explain what worked, what failed and why.
• Interest in translating research into capabilities on real robots, working closely with engineers across perception, autonomy and control.

Particularly useful experience

• Action-conditioned video or latent dynamics models, self-supervised visual representations, or model-based planning.
• Vision-language-action models, imitation learning or reinforcement learning.
• Training on robot demonstrations and interaction data, and deploying learned systems on physical robots.
• Efficient inference, distributed training or large-scale video and robotics datasets.

We welcome relevant experience from industry, academic research or substantial independent work. A PhD is welcome but is not a requirement where equivalent research and engineering depth is demonstrated.