Materials Science Jobs

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

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.

NVIDIA · Meta

• 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
• 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

• San Francisco, United States
• Hybrid
• 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.

TensorFlow · MLflow · Spark +6

• 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

• 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

• 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

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

Bridges frontier AI models and high-performance computing infrastructure by profiling large-scale training runs to improve Model FLOP Utilization and remove bottlenecks. Builds observability dashboards, optimizes NVIDIA GPU kernels and NCCL communication, and applies quantization, pruning and distillation to cut inference latency. Works closely with AI researchers and the infrastructure team on future cluster design.

NVIDIA · NCCL · Triton +7

• 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
• Mid-level
• Permanent

Early security hire building automated security pipelines and AI-specific guardrails to protect frontier models, training data and proprietary IP. The role spans securing the ML lifecycle against prompt injection and data poisoning, embedding DevSecOps checks into CI/CD, and hardening cloud and Kubernetes environments with least-privilege IAM.

GitHub Actions · GitLab CI · Jenkins +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

• 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

• New York, United States
• Hybrid
• US$150,000 – US$190,000 pa
• Mid-level

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 analytics environments, and translating R&D outputs into reusable libraries and products. Travels to customer sites roughly 3-4 weeks per quarter.

TensorFlow · MLflow · Spark +6