Materials Science Jobs

• 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

• 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

• New York, United States
• Hybrid
• US$200,000 – US$300,000 pa
• Lead

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.

GitHub · GitLab · Semgrep +6

• New York, United States
• Hybrid
• US$200,000 – US$250,000 pa
• Senior

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.

Python · PyTorch · Pandas +6

• New York, United States
• Hybrid · 2–3 days in office
• 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.

NumPy · SciPy · Pandas +2

• 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 through solver setup, HPC runs and experimental validation, while building parametric CAD and Python automation for design optimisation and AI surrogate workflows.

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

• Singapore
• Mid-level

Contributes to high-fidelity CFD simulations for marine propulsion projects, including propeller and hull-propeller interaction, using OpenFOAM for steady-state and transient cavitating cases. Builds models from geometry clean-up and meshing through post-processing, and works at the intersection of CAE and data science to generate simulation datasets for machine learning surrogates. Joins at the founding stage of the Singapore team, collaborating with data scientists, ML engineers and customers.

OpenFOAM · Star-CCM+ · Fluent +4

• Singapore
• Mid-level

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.

• New York, United States
• Hybrid · 2–3 days in office
• Mid-level

Builds and runs high-fidelity CFD models from geometry clean-up and meshing through simulation and post-processing, using Star-CCM+, OpenFOAM or Fluent on cloud and HPC resources. Works with data scientists and ML engineers to generate simulation datasets for deep learning surrogates, supporting customer engagements with occasional international travel.

Star-CCM+ · OpenFOAM · Fluent +4

• London
• Hybrid
• Lead

Own high-level research work-streams, setting technical direction and roadmaps while guiding junior team members. Build deep learning and probabilistic models of physical systems, translating physics and engineering challenges into mathematical formulations with ML and simulation engineers. Publish and present work internally and externally, and help shape research group strategy.

PyTorch · CUDA · JAX +5

• New York, United States
• Hybrid
• US$200,000 – US$300,000 pa
• 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 multiscale simulation for industrial materials and process problems.

DFT

• 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.

• Singapore
• Hybrid
• Lead

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.

NumPy · SciPy · Pandas +2

• Singapore
• Hybrid
• Senior

Build physical foundation models for industrial engineering, scaling training to large datasets on multi-GPU cloud compute. Turn research prototypes into robust, optimised implementations, own research workstreams, and translate results into reusable libraries and tooling alongside scientists and customers.

PyTorch · JAX · NumPy +7