Jobs at PhysicsX

Lead development and deployment of deep learning and probabilistic models for multi-physics engineering problems. Architect scalable data pipelines, collaborate with simulation engineers, drive R&D, mentor junior team members, and work directly with customers to integrate models into production, including onsite customer collaboration and presentations.

As a Forward Deployed Software Engineer, you will develop applications leveraging physics AI models, collaborating with data scientists and customers, and design E2E solutions while deploying APIs and microservices.

Lead development and deployment of high-fidelity multiphase CFD and coupled-physics models for industrial processes. Own geometry cleanup, meshing, solver setup, automation, HPC execution, surrogate modelling, DoE, experimental validation, and customer-facing delivery to translate simulation results into engineering recommendations.

Build and run CFD models from geometry cleanup and meshing through simulation and post-processing for simple to moderately complex cases. Support automation, parametric CAD adaptation, and dataset generation for ML. Work with customers on-site, use cloud and on-premise HPC, and contribute to simulation engineering best practices.

The Senior CFD Engineer will build complex multi-physics models, work with clients on engineering challenges, and deliver high-fidelity simulations, while mentoring junior colleagues and integrating AI tools.

The Principal Machine Learning Engineer will lead efforts in deploying ML models, architecting data systems, mentoring teams, and driving project scoping with a strong focus on customer collaboration.

As a Senior Forward Deployed Software Engineer, you will build and deploy applications utilizing AI-driven physical models to optimize engineering processes for clients across diverse industries.

Conduct research and build ML models (neural operators, geometric deep learning, generative models) to predict physical system behavior; own research streams, collaborate with engineers and customers, run experiments, publish results, and mentor peers.

Collaborate with research scientists and customers to design, train, and productionise physical foundation models. Scale multi-GPU training, convert prototypes into robust implementations, choose tooling and frameworks, own research workstreams, and translate research into reusable libraries, tooling and products while mentoring colleagues.