Improbable-2

Applied Scientist

Hybrid

Full Time

#Engineering

#Security

#AI

#Machine Learning

#Distributed Systems

#Unity

#Python

#Java

#C++

Improbable-2 is looking for a Applied Scientist

Full Time Senior Hybrid Engineering Security AI Machine Learning Distributed Systems Unity Python Java C++English Hybrid work

Join Improbable U.S. Defense & National Security and you will help users leverage our synthetic environment development platform to plan and train for the most complex security threats in the world. Working along experts in AI, Machine Learning, computational modeling, and distributed systems, you will put multiplayer gaming technology to life-saving use.

Our mission? To enable the most realistic and effective virtual worlds ever experienced. Our team in Arlington, VA, and across several other locations in the US, is focused on applying our technology to solve real-world Defense and National Security problems.

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

Our applied scientists are solution focused, collaborating in diverse technical teams to design, build, deploy, and evaluate complex models and simulations that help solve a variety of real-world problems. Your work could entail anything from the creation of realistic synthetic entities and environments to modeling real world systems.

Areas for Impact

Bring together best in class models to build and iterate on the most appropriate application of Modeling & Simulation.

Iteratively design and prototype models of real-world systems that enable our customers to interrogate the past, understand the present, and predict the future.

Collaborate with engineers to implement high performance models in production environments.

Work closely with Improbable's product team to enhance our foundational technology platform.

Critically assess the type and quality of customer or open-source data and define modeling assumptions and capture uncertainty.

Take ownership of ensuring and demonstrating the reliability of the models that we create.

Work closely with our technology customers to understand their problems and develop use cases for Improbable technology.

Additionally, you will drive thought leadership within the company and help our clients understand their needs and design their model and simulation strategies. This involves keeping up to date with a diverse body of cutting-edge research, designing and prototyping new technical approaches, and rapidly developing expertise in new subject areas to support new bids and projects.

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

Strong background and experience in delivery of technical, data-rich projects ideally for external clients.

Degree in a scientific, engineering, or mathematical field, ideally with a computational element.

Demonstrated knowledge in a statistical modeling and analysis.

Demonstrated experience with agent-based modeling, micro-simulation, computational social science, or modeling socio-technical systems.

Experience using game engines such as Unity and Unreal.

Pragmatic coding ability - with fluency in at least one relevant programming language and the openness to learn and adapt to a variety of languages. We use Python, R, Java, C, C++, and many related tools and libraries.

Breaking down problem requirements, selecting effective analytical methods, and developing defensible solutions.

Probabilistic programming. You can see our early open-sourced work here.

Working with real, complex data, including geospatial data.

Effectively communicating with clients in person, in writing, and with visualization.

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!

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Applied Scientist at Improbable-2