Engineering teams face an impossible reality: deliver more complex products faster, with fewer experts, and zero tolerance for failure. nTop changes how engineering gets done. Our technology collapses months of iteration into hours, letting teams explore thousands of variants instead of settling for the first option. Teams reduce development time by 50% and increase program win rates. Leaders choose nTop when failure isn't an option.

If you’re motivated by solving tough engineering challenges alongside a team that learns and grows together, you’ll thrive at nTop. We’re seeking teammates who are eager to experiment, innovate, and make a meaningful impact with technology.

Does this sound like you? nTop is hiring a Senior Software Engineer, Machine Learning in Applied Physics to build cutting-edge surrogate models that accelerate complex physics simulations by 100-1000x, enabling real-time engineering design and optimization.

This role is remote in either Germany or the UK, and reports to the Engineering Manager, Analyze.

What You'll Do

Build and deploy surrogate models to replace expensive physics simulations with 100-1000x speedups
Integrate existing ML solutions into the nTop ecosystem
Prepare and curate training data by labeling simulation outputs, annotating physics features, and building high-quality datasets from multi-fidelity sources
Validate models rigorously against high-fidelity simulations and experimental data with uncertainty quantification
Document model architectures, assumptions, and limitations for technical and non-technical stakeholders

Required Experience

MS/PhD in Physics, Applied Mathematics, Mechanical/Aerospace Engineering, or related computational field
3+ years developing surrogate/reduced-order models for physical systems
Proven expertise in deep learning frameworks (PyTorch or TensorFlow) applied to scientific problems
Strong foundation in numerical methods, PDEs, and computational physics (FEM, CFD, or similar)
Proficiency in Python scientific computing stack (NumPy, SciPy, Pandas, scikit-learn)
Experience with uncertainty quantification and Bayesian methods in ML models
Track record of working with large-scale simulation data and HPC environments
Hands-on experience with at least one physics simulation package (ANSYS, COMSOL, OpenFOAM, etc.)

Preferred Experience

Experience with neural operators (DeepONet, Fourier Neural Operators) for PDE solving
Experience with NVIDIA ecosystem for physics AI (Physics Nemo, Omniverse, SimNet)
Familiarity with NVIDIA Domino for ML model management and deployment
Experience with setting up and deploying inference services
Proficient in containerization technologies, particularly Docker
Background in aerospace and energy systems
GPU programming experience (CUDA, OpenCL)
Understanding of digital twin architectures and real-time simulation requirements

Compensation