Senior Machine Learning Software Engineer in Applied Physics

NLP PEOPLE; Liverpool; On-site; GBP 61,000 - 70,000; Full time

Job summary

A technology company located in the UK is seeking a Senior Machine Learning Software Engineer in Applied Physics to drive innovation in engineering design. You will develop surrogate models, enhancing simulation efficiency by significant factors. Candidates should have a MS/PhD in a relevant field and substantial experience with deep learning frameworks and physics simulations. The position offers a competitive salary range of €70,000–€80,000 annually, along with options for growth.

Qualifications

• 3+ years of experience developing surrogate or reduced-order models for physical systems.
• Demonstrated capability in handling large-scale simulation data within high-performance computing environments.
• Hands-on experience with physics simulation software.

Responsibilities

• Develop and deploy surrogate models to replace intensive physics simulations.
• Integrate current machine learning solutions into the nTop technology ecosystem.
• Conduct rigorous validation of models against high-fidelity simulations.

Skills

Deep learning frameworks (PyTorch, TensorFlow)

Python scientific computing stack (NumPy, SciPy, Pandas, scikit-learn)

Numerical methods, PDEs, computational physics

Experience with physics simulation software (ANSYS, COMSOL, OpenFOAM)

Uncertainty quantification and Bayesian techniques

GPU programming (CUDA, OpenCL)

Education

MS/PhD in Physics, Applied Mathematics, or Engineering

Job description

Join our dynamic team at nTop, where we’re revolutionizing the engineering landscape with our innovative technology. We’re seeking a Senior Machine Learning Software Engineer in Applied Physics to contribute to cutting‑edge projects that greatly enhance the efficiency of engineering design and simulations.

In this role, you’ll tackle complex challenges and work collaboratively within a team that prioritizes learning and growth. Your contributions will help us accelerate physics simulations by 100‑1000 times, drastically improving real‑time engineering design and optimization processes.

What You’ll Do

• Develop and deploy surrogate models to replace intensive physics simulations, achieving impressive speedups of 100‑1000x.
• Integrate current machine learning solutions seamlessly into the nTop technology ecosystem.
• Prepare and curate training datasets by annotating simulation outputs and extracting relevant physics features from multiple fidelity sources.
• Conduct rigorous validation of models against high‑fidelity simulations and experimental data, including uncertainty quantification.
• Document model architectures, assumptions, and limitations clearly for both technical and non‑technical stakeholders.

Required Experience

• MS/PhD in Physics, Applied Mathematics, Mechanical/Aerospace Engineering, or a related computational area.
• 3+ years of experience developing surrogate or reduced‑order models for physical systems.
• Significant expertise in deep learning frameworks (such as PyTorch or TensorFlow) applied to scientific problems.
• Strong foundational knowledge in numerical methods, PDEs, and computational physics like FEM or CFD.
• Proficient in the Python scientific computing stack: NumPy, SciPy, Pandas, and scikit‑learn.
• Experience with uncertainty quantification and Bayesian techniques in machine learning models.
• Demonstrated capability in handling large‑scale simulation data within high‑performance computing environments.
• Hands‑on experience with at least one physics simulation software package (ANSYS, COMSOL, OpenFOAM, etc.).

Preferred Experience

• Familiarity with neural operators (such as DeepONet and Fourier Neural Operators) for PDE solving.
• Experience with NVIDIA’s ecosystem for physics AI (including Physics Nemo, Omniverse, SimNet).
• Knowledge of NVIDIA Domino for machine learning model management and deployment.
• Experience in setting up and deploying inference services.
• Proficiency in containerization technologies, especially Docker.
• A background in aerospace and energy systems is a plus.
• GPU programming experience (using CUDA or OpenCL).
• Understanding of digital twin architectures and requirements for real‑time simulation.

Compensation

• €70.000‑€80.000 annually plus options.

Company

nTop

Senior (5+ years of experience)

Tags

• Industry
• Machine Learning
• NLP
• United Kingdom