26 jobs
PhysicsX New York, United States US$150,000 – US$250,000 pa
Build and deploy customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. The role spans rapid prototyping, backend development with Python/FastAPI microservices, and cloud deployment using Kubernetes, Terraform and AWS/Azure, with frequent on-site travel to customer sites.
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 run the product vulnerability lifecycle. Also drives secure coding standards, delivers hands-on training, and engages with customers during security reviews.
Cohere London
Builds and owns the training framework powering frontier-scale language models, spanning distributed training abstractions, checkpointing, and performance profiling across thousands of GPUs. Works at the intersection of large-scale training, distributed systems, and HPC, partnering with infra, research, and deployment teams on tooling that speeds up ML development.
Cohere London
Drive safety research for agentic LLMs, focusing on data generation, post-training algorithms and evaluation methods so models that access external tools and act in the world remain fair, trustworthy and secure. Work with cross-functional ML, data annotation, product and policy teams on a small, autonomous team, requiring strong statistics, software engineering and Python/PyTorch skills.
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, optimisation) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Works directly with customers on-site, leads scoping and architecture, and mentors engineers and data scientists. Tech stack includes Python, PyTorch, Pandas, fastAPI, Scipy and Kubeflow.
PhysicsX Singapore
Develops multiphase CFD and DEM models for industrial bioprocess and chemical engineering applications, covering gas-liquid, solid-liquid, VOF and Euler-Euler/Euler-Lagrange approaches, non-Newtonian rheology and coupled reactive physics. Owns meshing strategy, parametric CAD-coupled simulation pipelines, HPC-scale transient runs and surrogate/ML-CFD workflows, working directly with customers to scope and validate models against experimental data.
PhysicsX London
Builds and validates multiphase CFD and coupled CFD-DEM models, covering geometry clean-up, meshing, simulation, post-processing and experimental validation for granular and particle-laden systems. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for ML surrogate models on cloud and HPC resources, while working on-site with customers and mentoring junior colleagues.
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 develop deep learning and probabilistic models for physical systems, run large-scale experiments, and own research workstreams while publishing and presenting to customers and the community.
PhysicsX Singapore
Build physical foundation models for industrial engineering, scaling training across multi-GPU cloud compute and turning research prototypes into robust, optimised implementations. Work at the intersection of data science and software engineering, owning research work-streams and translating results into reusable libraries, tooling and products alongside scientists, customers and partners.
PhysicsX Singapore
Builds and operates a SLURM-on-Kubernetes HPC platform running 304 B200 GPUs for large-scale distributed training and CAE data generation. Splits time between platform engineering — hardening, storage, networking, observability — and hands-on debugging of job failures and throughput bottlenecks alongside researchers.
PhysicsX London
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments, using Python, PyTorch, Pandas, fastAPI, Scipy and Kubeflow across cloud and on-prem. Leads scoping and architecture for data/ML systems, mentors engineers and data scientists, and travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX Singapore
Builds and delivers high-fidelity multi-physics CFD models, from geometry clean-up and meshing to simulation and post-processing, integrating experimental data for validation. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for machine learning surrogates. Works on-site with customers and mentors junior colleagues, with global travel up to 2-3 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. Build scalable production data pipelines, mentor junior team members, and collaborate on-site with customers across multiple regions to embed AI models into engineering tools.
PhysicsX Singapore
Lead the deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments. Own scoping, architecture and success metrics for data/ML systems, mentor engineers, and work on-site with customers across North America, Europe, Asia and Oceania for 3-4 weeks per quarter.
PhysicsX Singapore
Build and deploy customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. The role spans rapid prototyping, backend development of Python/FastAPI REST APIs and microservices, and cloud deployment using Kubernetes, Helm, Terraform and AWS/Azure. It suits someone who can grasp complex engineering problems across industries and own solutions end-to-end.
