Materials Science Jobs in York

CureAge Therapeutics · London

Lead the computationally guided design of a lipid nanoparticle delivery platform, applying chemical and biophysical principles to optimise LNP composition and formulation. Use AI-enabled chemistry, cheminformatics, predictive models and molecular modelling to prioritise formulation components, working closely with the experimental team and founding team in a small start-up.

PhysicsX · New York, United States · US$240,000 – US$300,000 pa

Leads regional deployment engineering for a physics AI platform, owning end-to-end delivery into customer-owned infrastructure and acting as primary technical contact for CIOs and CTOs. Combines executive engagement with hands-on work on Kubernetes, cloud and infrastructure-as-code deployments, while hiring and scaling the regional team. Requires navigating regulated, high-security and on-prem/HPC environments.

PhysicsX · New York, United States · US$150,000 – US$250,000 pa

Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, backend development of FastAPI microservices in Python, and cloud deployment with Kubernetes, Terraform and AWS/Azure. Requires travel to customer sites for 3-4 weeks per quarter.

PhysicsX · London

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

PhysicsX · Singapore

Deliver high-fidelity multi-physics CFD simulations for industrial customers, from geometry clean-up and meshing to post-processing and validation against experimental data. Build parametric CAD models and automate simulation pipelines for design optimisation and DoE studies, generating datasets for machine learning surrogates. Work alongside data scientists and ML engineers, mentor junior colleagues, and travel globally up to 2-3 weeks per quarter to work on-site with customers.

PhysicsX · Singapore

Lead technical delivery of physics AI solutions, architecting deep learning models and optimisation methods to predict and control physical systems. Work closely with simulation engineers and customers, building scalable production data pipelines and mentoring junior team members. Requires frequent travel to customer sites across multiple regions.

PhysicsX · Singapore

Lead the deployment of ML models and engineering surrogates—deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, and optimization—into customer production environments. Own scoping, architecture, and success metrics for data/ML systems built with Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow across cloud and on-prem. Mentor engineers and data scientists while travelling 3-4 weeks per quarter to customer sites worldwide.

PhysicsX · Singapore

Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, RESTful APIs in Python (FastAPI), microservices and cloud deployment with Kubernetes, Helm, Terraform and AWS/Azure. Spans the full stack across complex engineering domains.