About us

Re-architecting Engineering for the Age of Intelligence

PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.

Who We're Looking For

As a Data Scientist in Delivery, you are a problem solver and builder who is passionate about creating practical solutions that enable customers to make better engineering decisions. You are someone who can grasp advanced engineering concepts across multiple industries, and you excel at working directly with customers (and often side-by-side with them on-site) to transform cutting edge AI models into tools that are useful and used.

You’ve worked on difficult problems that require strong foundations in data driven modelling and deep learning techniques, with hands-on experience in probabilistic methods and predictive modelling. Expertise in python, along with proficiency in libraries like NumPy, SciPy, Pandas, TensorFlow and PyTorch, is essential, with the ability to deploy scalable, production-ready models and data pipelines.

With at least 1 year industry experience (post Masters or PhD) in a commercial, non-research environment, you’re ready to hit the ground running. You’re truly excited about growing your technical expertise and are naturally inclined to take ownership of data science work streams, continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

Note: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.

This Role

In this role, you’ll work closely with our Simulation Engineers, Machine Learning Engineers, and customers to understand and define the engineering and physics challenges we are solving.

You’ll build the foundations for successful, impactful solutions by:

Pre-processing and analyzing data to prepare it for use in predictive modelling, building the foundation for machine learning algorithms to be developed.
Developing and utilizing innovative deep learning models in combination with state-of-the-art optimization methods to predict and control the behaviour of physical systems.
Taking full responsibility for the quality, accuracy and impact of your work.
Designing, building and testing data pipelines that are reliable, scalable and easily deployable in production environments.
Working closely with simulation engineers to ensure seamless integration of data science models with simulations.
Contributing to internal R&D and product development, helping to refine models and identify new areas of application.
Engaging in open communication and presentation with both technical teams and customers, helping onboard users and co-develop with customers.

You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.

As the role evolves, there are exciting opportunities for growth as an individual contributor or a technical lead, especially if you’re driven by taking ownership of more complex projects and leading the direction of future solutions.