62 jobs for “Energy Transition”
PhysicsX New York, 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 analytics environments, and translating R&D outputs into reusable libraries and products. Travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX New York, United States US$200,000 – US$300,000 pa
Partners with engineering teams to embed security into CI/CD pipelines, lead threat modeling and secure design reviews for web apps, APIs and cloud services, and manage the product vulnerability lifecycle. Requires deep DevSecOps and application security expertise, with hands-on Python and Go development and SAST tooling.
CuspAI London
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.
CuspAI London
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.
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.
CuspAI Cambridge
Trains and evaluates multi-modal foundation models for a materials discovery platform, spanning crystal structures, properties, text and images. Owns a model end to end, from data curation and experiment design through large-scale distributed training to integration, working closely with chemists and materials scientists.
CuspAI London
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.
CuspAI London
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.
CuspAI London
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.
