Senior AI Engineer (Physics AI)

Location: Singapore or Sydney

Company

hirify.global Technologies develops and operates energy-efficient AI infrastructure across Asia Pacific, including AI Factories and the hirify.global AI Cloud GPU platform.

What you will do

• Develop and qualify surrogate models for thermal, airflow, cooling and infrastructure scenarios using convolutional models, neural operators and graph-based approaches.
• Build reproducible pipelines from Ansys Fluent and other engineering tools into model-ready datasets while preserving geometry, mesh, equipment, boundary-condition, unit and solver-version lineage.
• Deliver production software including training pipelines, model registries, qualification workflows, inference APIs, model packaging, automated testing, monitoring and technical documentation.
• Deploy surrogate model endpoints through the AI & Applications inferencing platform on shared GPU capacity and integrate outputs with optimisation, design records and visualisation services such as Omniverse and OpenUSD.
• Define acceptance criteria, evaluate accuracy and physical consistency, implement uncertainty and out-of-distribution checks, and provide rejection or solver-fallback behaviour.
• Integrate models into scenario screening, design comparisons, commissioning rehearsal and operating-envelope studies across compute, cooling, power and infrastructure systems.

Requirements

• Deep hands-on experience in scientific machine learning, computational engineering or simulation-led software, with experience taking models from research into dependable production services across multiple projects.
• Technical leadership of at least one cross-disciplinary programme spanning machine learning, simulation and software delivery.
• Strong Python and PyTorch skills, including developing, training, profiling, evaluating and deploying models on GPU infrastructure.
• Experience with surrogate modelling, neural operators, graph neural networks, reduced-order modelling, physics-informed learning or closely related methods.
• Comfort working with simulation data, meshes, geometry, boundary conditions and numerical validation, including understanding discretisation, convergence, conservation, data coverage and measurement uncertainty.
• Ability to distinguish interpolation from unsupported extrapolation and determine when a surrogate should defer to an authoritative engineering calculation.
• Experience building maintainable software with APIs, containers, version control, automated tests, CI/CD and observability.
• Ability to design reproducible data and model contracts, diagnose failure modes and collaborate across engineering disciplines.
• Degree in engineering, computer science, applied mathematics, computational physics or a related discipline, or equivalent practical depth.

Nice to have

• Experience with NVIDIA PhysicsNeMo or Modulus, PyTorch Geometric, graph or operator learning, uncertainty quantification and multi-GPU training.
• Familiarity with Ansys Fluent or PyFluent, OpenFOAM, ETAP or comparable engineering tools, CAD-to-simulation workflows, OpenUSD, Omniverse, Kubernetes or on-premises accelerated computing.
• Experience with data-centre thermal systems, liquid cooling, electrical infrastructure or GPU/HPC environments.
• Experience acting as a technical anchor across simulation, machine learning and software teams by setting standards, reviewing designs and mentoring engineers.

Culture & Benefits

• Permanent full-time employment in a founder-led organisation with fast decision-making and limited bureaucracy.
• Direct collaboration with founders and specialists in AI infrastructure, energy systems and next-generation compute.
• Opportunities to work across AI infrastructure, energy management, liquid cooling, software orchestration and construction.
• Focus on sustainable AI infrastructure designed to improve the efficiency and resilience of energy systems.
• Inclusive workplace committed to supporting candidates from diverse backgrounds.