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Develop machine learning models for real-world physics problems, optimize model performance, implement distributed training, and mentor less experienced colleagues.

Lead deployment of production ML systems for physics-based engineering: design scalable training/inference pipelines, work with 3D point-clouds and mesh data, collaborate with customers onsite, mentor engineers, define architecture and success metrics, and deliver robust cloud and on-prem ML solutions.

The Senior Machine Learning Engineer will lead the deployment of ML models, mentor engineers, and influence product decisions, while collaborating directly with customers on-site to solve engineering challenges across various industries.

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 Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Senior Machine Learning Engineer will design, deploy, and mentor on ML models, collaborate with customers, and influence product direction. Responsible for robust ML systems and data pipelines.

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.