Research Scientist

14 days ago


Improbable believes in a future where new, virtual worlds will augment human experience and become as meaningful, lasting and rich as the physical world. We call this the Multiversal Self.

Our platform, SpatialOS, lets developers transcend the limits of regular computation, allowing swarms of servers running in the cloud to cooperate in order to simulate worlds far larger and more complex than any single server could.

At Improbable, you are surrounded by people who want to improve everything and everyone around them, and who compel you to improve yourself. We’re motivated by the fulfilment of solving hard problems to achieve something profound and transformative.

Your Mission

is to help us achieve step changes in capability for simulations of unprecedented realism and complexity, for the purpose of ‘in-silico’ experimentation with the effects of aggressive and defensive action on socio-technical systems: the intertwined systems that make up the environment and society. Via R&D in machine learning, statistical inference and computer science, we produce novel methodology and tooling for modellers of complex systems.

We are a team of experienced researchers and software engineers interested in removing computational and information bottlenecks in the design and validation of models of complex systems, typically aimed at counterfactual analysis and simulation.

We aim to ensure that models of complex systems are sound, statistically calibrated against real-world evidence, and built in a modular fashion to ensure that more complex models can be synthesized by combining models of simpler components. We are also interested in policy optimisation and reinforcement learning strategies for multiple autonomous agents operating within this synthetic environment. This puts our work at a crossroads between agent-based modelling, multi-agent systems and complex systems.

We collaborate with some of the leading academic research groups in the UK. We take inspiration from the success of general-purpose tuning algorithms and resulting frameworks for deep learning (e.g., Tensorflow) and hierarchical Bayesian models (e.g., Stan or Pyro), and aspire to build analogous tools for modelling complex agent systems. Our prior work includes an in-house probabilistic programming language, Keanu.

Areas for Impact

We'd like to hear from you if you identify with the following:

While we think the above experience could be important, we’re keen to hear from people that believe they have valuable experience to bring to the role. If you identify with the team and mission, but not all of our requirements, then please still apply!!

Equal Opportunity

The best ideas are often the least expected and require new ways of thinking; that’s why our teams at Improbable are made up of an incredible range of talented people. Improbable is proud to be an equal opportunity employer. We do not discriminate based on race, ethnicity, colour, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.

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