62 jobs for “Energy Efficiency”
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.
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.
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.
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.
PhysicsX New York, United States US$200,000 – US$250,000 pa
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimization) 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.
PhysicsX New York, United States US$120,000 – US$240,000 pa
Builds and deploys deep learning and probabilistic models to predict and control physical systems, working alongside simulation engineers and customers. Owns data pipelines and production-ready models, and travels to customer sites 3-4 weeks per quarter to co-develop solutions on-site.
PhysicsX New York, United States US$150,000 – US$250,000 pa
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, plus 3-4 weeks of travel per quarter to customer sites.
PhysicsX Singapore
Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate industry challenges into product opportunities, tracking engagement and measuring value. Suits a technical background in data science/ML or engineering.
PhysicsX New York, United States
Builds and runs high-fidelity CFD models from geometry clean-up and meshing through simulation and post-processing, using Star-CCM+, OpenFOAM or Fluent plus parametric CAD and Python/Java automation. Works with data scientists and ML engineers to generate simulation datasets for deep learning surrogates, running jobs on cloud and HPC, and supports customer engagements with occasional travel.
PhysicsX Singapore
Join a new Singapore research team building physical foundation models for industrial engineering, translating physics and simulation challenges into mathematical formulations. You will train deep learning and probabilistic models on large spatiotemporal, geometric and point-cloud datasets, run experiment pipelines, and publish and present results with customers and partners.
PhysicsX London
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.
PhysicsX London
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.
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.
