PhysicsX | Principal AI Engineer | London / NY / Remote | Full time | physicsx.ai
We're building an AI-native platform that turns physics simulations from days-long jobs into millisecond inference. Think: training deep learning surrogates on CFD, FEA, thermal data, and Large Physics Model (LPM) frontier research so engineers can explore thousands of design variants in a single session — across aerospace, semiconductors, energy, and automotive.
You should have the scar tissue of running agents in production and strong opinions on what the future looks like.
We're ~170 people, raised $135M Series B, and backed by General Catalyst, Atomico, Siemens, and Applied Materials. The team includes people from F1, SpaceX, Google, Palantir, and Tesla.
Big fan of evidence, really elegant design. There is also space for both declarative and imperative approaches when it comes to dashboarding / reporting etc.
Kedro sort of fits into a niche where it just overlaps somewhat with 'orchestrators' like Prefect, Metaflow, Dagster, Airflow and others. What makes it slightly different is that it it is focused on the rapid development journey to production, providing guardrails for teams to co-develop ML projects in a way that nudges software engineering best practice and clean code.
The 'finished article' in many cases should be deployed in production in one of those tools which provide specialised bells and whistles like scheduling, monitoring and observability.
Regarding MLFlow, there is also a slight overlap in terms of experimentation, but not things like model serving. Kedro has a mechanism to track experiments, but it's more designed to give users with zero infrastructure something for free out of the box. It's been built in a way that it can be repurposed for more dedicated experiment tracking tools - the folks at neptune.ai built their own plug-in for this purpose: https://docs.neptune.ai/integrations-and-supported-tools/aut...
Donation is the normal term for proejcts joining the foundation - in doing so you establish a steering committee where members are entitled to voting rights. To graduate as an incubation project, 5 organisations need to join your board and the project is thus the priorities of the project will no longer be driven by just one organisation.
In the short term there is still a full time internal team staffed and maintaining the project, but excitingly we now have a mechanism for new collaborators to properly come onboard.
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
Join users such as NASA, Telkomsel, XP Investimentos, Sber and Beamery!