Materials Science Jobs

• Cambridge
• Hybrid · 3 days in office
• Permanent

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.

Git

• Huntingdon
• On-site
• Entry level
• Permanent

Undergraduate placement supporting research, production or electronics engineering, with hands-on work on graphene and 2D material devices. Tasks span AFM and Raman characterisation, Hall measurements, device manufacture, test system design, prototyping and debugging, plus lab upkeep and documentation. Based at a Cambridgeshire manufacturer with R&D and production sites.

Graphene · 2D Materials · Semiconductor Processes

• London
• Hybrid · 3 days in office
• Mid-level
• Permanent

Owns a set of partner labs and their integration onto an AI materials-discovery platform, translating experimental workflows, instruments and LIMS/ELN data into engineering requirements. Leads technical discovery and onboarding through to first closed-loop campaigns, and works with ML researchers on Bayesian optimisation campaigns that respect real lab constraints.

Python · LIMS · ELN +3

• London
• Hybrid · 2–3 days in office
• Mid-level

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.

JAX · NVIDIA Warp · OpenFOAM +2

• New York, United States
• Hybrid
• Lead

Lead the ML approach to manufacturing and MRO data, from modelling through deployment in customer production environments across cloud, on-prem and edge. Build production ML on sparse, drifting sensor and process data, set uncertainty quantification and validation standards, and mentor engineers while travelling to customer sites worldwide.

PyTorch · Pandas · FastAPI +2

• Cambridge
• On-site
• Senior
• Permanent

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.

HTRB · HTOL

• Singapore
• Senior

Builds and applies advanced thermal, chemical and multiphase CAE models in Abaqus, ANSYS or COMSOL, coupling heat transfer with fluid, diffusion and electrostatic phenomena. Works directly with customers, often on-site, instrumenting physical processes to generate validation data and integrating models into AI tools. Combines shop-floor manufacturing experience with Python/Java automation and parametric CAD.

Abaqus · ANSYS Mechanical · ANSYS Fluent +4

• London
• Hybrid
• Mid-level

Owns the editorial calendar and writes blog posts, web pages, thought leadership, scripts and event materials, translating deep technical work into clear narratives. Leads LinkedIn and Instagram, supports executive and technical speaking engagements, and partners with engineers, designers and external agencies on video and joint content.

LinkedIn · Instagram · Figma +1

• London
• Hybrid · 3 days in office
• Senior

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.

Python · PyTorch · Pandas +6

• Singapore
• Lead

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.

Star-CCM+ · OpenFOAM · Fluent +3

• Singapore
• Lead · manages people

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.

NumPy · SciPy · Pandas +2

• Singapore
• Lead

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.

Python · PyTorch · Pandas +6

• Singapore
• Senior

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.

FastAPI · Helm · Kubernetes +3