Artificial Intelligence Jobs

• London
• Hybrid · 3 days in office
• Lead

Design and evolve a large-scale ML infrastructure platform that supports distributed training and inference workloads, working closely with research, engineering and product teams. The role also explores deploying agents to improve platform robustness and reliability, in a hybrid London setup requiring three office days a week.

Kubernetes · Google Cloud Platform · GCP

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

Join a newly formed Scheduling team to own and extend systems that orchestrate compute allocation for complex ML workloads on Kubernetes. You'll design scheduling policies like gang scheduling and topology-aware placement, build custom Kubernetes controllers and CRDs, and partner with ML researchers to maximize GPU/TPU utilization.

Kubernetes · Kueue · Volcano +5

• Singapore
• Hybrid
• Lead

Develops high-fidelity multiphase CFD and DEM models for industrial bioprocess and chemical engineering applications, covering gas-liquid, solid-liquid, VOF and Euler-Euler/Euler-Lagrange approaches. Owns the full simulation pipeline from geometry clean-up and meshing to solver setup, HPC job scheduling and experimental validation, while building parametric CAD models and AI surrogate models to support design optimisation and customer-facing delivery.

Star-CCM+ · OpenFOAM · Fluent +5

• 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.

FastAPI · Helm · Kubernetes +3

• 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 with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

Python · PyTorch · Pandas +6

• Singapore

Build and deploy customer-facing applications powered by physics AI models, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, Python/FastAPI microservices, and cloud deployment with Kubernetes, Terraform and AWS/Azure, plus quarterly travel to customer sites.

FastAPI · Helm · Kubernetes +3

• Singapore
• Lead

Deliver high-fidelity multi-physics CFD simulations, 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, generating datasets for machine learning surrogates. Partner directly with customers on-site and mentor junior colleagues.

Star-CCM+ · OpenFOAM · Fluent +4

• Singapore
• Lead · manages people

Lead technical delivery of physics-informed AI, architecting deep learning models and optimisation methods to predict and control physical systems. Own data pipelines and pre-processing best practices while mentoring a team and collaborating with simulation engineers and customers. Involves frequent on-site customer travel 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 optimisation—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 +3

• Singapore
• Senior

Customer-facing engineering role building and deploying applications that put physics AI models into production for industrial clients. You will work directly with customers, data scientists and ML engineers, prototyping tools, iterating on user feedback, and building Python FastAPI microservices deployed on Kubernetes, Terraform and AWS/Azure.

Python · FastAPI · Helm +4

• New York, United States
• Hybrid
• US$150,000 – US$250,000 pa
• Lead

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 engineering and physics challenges into product opportunities, tracking engagement and growing strategic accounts.

• New York, United States
• Hybrid
• Lead

Lead the ML approach to manufacturing and MRO data, taking models from design through deployment in customer production environments across cloud, on-prem and edge. Work with sparse, drifting sensor and process data, fusing physics simulation with shop-floor measurement, and set uncertainty quantification and validation standards. Mentor engineers and data scientists while travelling to customer sites roughly 3-4 weeks per quarter.

Python · PyTorch · Pandas +3