32 jobs for “Semiconductors”
PhysicsX London
Build the simulation side of Large Physics Models, generating training data across fidelity levels and developing solvers in differentiable or GPU-native frameworks like JAX or Warp. Work in a research team alongside ML researchers, using commercial and open-source CFD tools to produce reference data and shape data-generation strategy.
PhysicsX San Francisco, United States US$150,000 – US$190,000 pa
Builds and deploys machine learning pipelines for industrial physics applications, working directly with customers on-site to embed AI models into practical tools. Involves manipulating 3D point-cloud and mesh data, cloud and HPC infrastructure, and translating R&D outputs into reusable libraries and products.
Cambridge GaN Devices Cambridge
Define and architect test hardware for GaN power devices and ICs, covering fixtures, sockets, probing, switching and multi-site parallel setups for static, dynamic and mixed-signal measurements. Lead test hardware workstreams from concept to release, write specifications, review designs, mentor engineers and shape the equipment roadmap.
CuspAI Cambridge
Owns scientific delivery of partner projects from scoping to results, acting as a key technical contact alongside programme managers. Applies simulation, machine learning, data-driven optimisation or experiments to design and steer discovery campaigns, and translates between experimental, computational and ML teams. Also builds cross-cutting capabilities such as validated workflows, harmonised data pipelines and agentic design campaigns.
CuspAI London
Builds and runs a global partner ecosystem for an AI materials platform, hosting events and managing relationships with tech partners, enterprise clients and research institutions across Europe, the USA and APAC. Bridges external partners with internal AI research, chemistry and product teams, channelling feedback into the agentic platform's roadmap.
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.
