Materials Science Jobs in London

• London
• Hybrid
• Director · manages people

Owns chemistry capabilities end to end, setting technical direction and leading a team across capability-building and customer delivery. Couples atomistic and molecular insight into continuum and systems-scale models, spanning DFT, machine learning interatomic potentials and process simulation for industrial materials problems.

• London
• Hybrid
• Mid-level
• Permanent

Builds and operates the software layer connecting an AI materials-discovery platform to partner laboratories worldwide. Designs APIs and data models for lab-to-cloud exchange, integrates instruments, robotic platforms and LIMS/ELN systems, and hardens integrations with retries, queueing and monitoring. Works in a small multidisciplinary team of materials scientists, ML and software engineers.

FastAPI · Pydantic · LIMS +5

• London
• Hybrid
• Mid-level
• Permanent

Builds and maintains a cloud-native ML infrastructure platform on Kubernetes, owning CI/CD pipelines, model registries, experiment tracking and deployment automation. Scales distributed GPU training for JAX and PyTorch workloads, manages multi-environment configs with Kapitan and Terraform, and drives platform reliability and cloud cost optimisation for research teams.

Kubernetes · AWS · GCP +7

• London
• £23,805 pa
• Entry level
• Contract

Builds physics-informed surrogate models and process simulations within a Bayesian-optimisation loop, jointly searching design and process parameters with active learning to minimise costly physical builds. Traces manufacturing process to defects to mechanical and electrochemical performance across hand lay-up, AFP and continuous-fibre 3D printing, starting on flat coupons before pushing into curved and cylindrical geometry.

AFP · 3D Printing

• London
• Hybrid
• Lead · manages people
• Permanent

Lead the Forward Deployed Engineering function, building and hiring a team while delivering complex CurieOS deployments hands-on. Write production code, design architectures and integrations across simulation, digital twins, lab instruments and CAD/PLM systems, embedding with customer R&D teams from discovery through production adoption.

CurieOS

• London
• £52,790 – £73,271 pa
• Senior
• Permanent

Deliver undergraduate materials and polymer science education for a joint UK-China engineering school, with a focus on curriculum development, novel pedagogies and inclusive teaching. The post is based in London but involves extensive travel to Xi'an, China for substantial periods, supporting transnational education programmes.

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

Builds and grows a global network of partner laboratories that synthesise materials designed with the company's AI platform, working with the Lab Integration team to enable automated, closed-loop discovery workflows. Acts as the operational bridge between partnership, science and AI research teams across Europe, the USA and APAC, and channels lab feedback into platform development priorities.

Robotics · Commercial Laboratory Automation Systems

• London
• Hybrid
• Permanent

Owns the full lifecycle of data-rich tools, services and web applications, building scalable ingestion, processing and cleaning pipelines for structured and unstructured materials-science data. Develops Python APIs and UIs for querying, exporting and visualising data, with orchestration, governance and containerised deployment using Docker and Kubernetes.

Dagster · Flyte · Prefect +6

• London
• Hybrid · 3 days in office
• Permanent

Owns the design of scientific data representations, schemas and ingestion pipelines that turn heterogeneous external materials data into ML-ready assets. Works with AI researchers and materials scientists to harmonise experimental and computational data from partners, defining quality, validation, lineage and provenance frameworks.

Pydantic · JSON Schema · pymatgen +7

• 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
• Entry level
• Permanent

Develop fast, accurate machine learning force fields distilled into lightweight student potentials for high-throughput Monte Carlo simulation, then integrate them into the in-house kUPS framework. The role involves curating training datasets, running validation campaigns against classical force-field baselines, and profiling the MC inner loop for throughput. Embedded in the chemistry team, with a publication on MLFF-driven GCMC for MOF screening.

kUPS · MLFFs · Equivariant Models +2

• London
• Hybrid · 3 days in office
• Senior
• Permanent

Designs and builds agentic frameworks that orchestrate closed-loop scientific workflows, connecting agents to ML models, simulation engines and databases. Applies Bayesian optimization and active learning for experimental design, and builds evaluations for agent planning and decision-making. Works closely with chemists and materials scientists on customer projects.

PyTorch · JAX · LLM