Full Time Senior Research Engineer Jobs, Salary up to SGD 10,603.6 in Qubot Technology - Maukerja

Qubot

Location: Shanghai

About the role

Qubot is looking for a Physics-AI Research Engineer to develop intelligent systems that connect machine learning with the physical world. You will combine physics, numerical simulation, and modern AI to solve problems in robotics, sensing, imaging, and control.

This is a hands-on research and engineering role: you will formulate problems, develop models, design experiments, and turn promising research into reliable software.

What you’ll do

• Develop physics-informed and hybrid models that combine physical principles with learned representations.
• Build differentiable simulations, surrogate models, and digital twins for physical systems.
• Apply machine learning to inverse problems, system identification, parameter estimation, and optimization.
• Combine simulated and experimental data to improve model accuracy and transfer results to real systems.
• Explore methods such as neural operators, physics-informed neural networks, differentiable programming, and learning-based control.
• Design rigorous evaluations covering physical consistency, generalization, uncertainty, and computational performance.
• Work with robotics, hardware, and software engineers to integrate models into experimental and production workflows.
• Reproduce relevant research, document findings, and contribute to publications or patents where appropriate.

What we’re looking for

• A strong background in physics, applied mathematics, engineering, computer science, or a related field, demonstrated through research or substantial practical work.
• Solid foundations in linear algebra, calculus, probability, numerical methods, and optimization.
• Practical experience with machine learning and at least one framework such as PyTorch or JAX.
• Strong Python programming skills and the ability to write readable, tested, reproducible research code.
• Experience modeling physical systems or solving scientific computing problems.
• Ability to translate an ambiguous real-world problem into a mathematical formulation, an experiment, and a measurable result.
• Clear communication and comfort working across disciplines.

Especially relevant experience

We welcome depth in one or more of the following; you do not need experience in all of them:

• Scientific machine learning: neural operators, differentiable solvers, learned surrogate models, or physics-informed learning.
• Robotics and control: dynamics, state estimation, model predictive control, reinforcement learning, or sim-to-real transfer.
• Imaging and inverse problems: reconstruction, calibration, computational imaging, or sensor fusion.
• Physical simulation: mechanics, electromagnetics, fluid dynamics, finite element methods, or multiphysics modeling.
• Research engineering: GPU computing, C++, CUDA, scalable experimentation, or deployment under real-time constraints.
• Research publications, open-source contributions, or demonstrated results on physical hardware.

What success looks like

You can establish a credible baseline, identify where physics or learning adds value, and demonstrate improvements through reproducible experiments. Your models hold up against measured data, their limitations are understood, and other engineers can build on your work.

How to apply

Share your résumé and one or two examples of relevant work—papers, code, projects, or experimental results. Include a short description of a physical problem you tackled, the methods you used, your individual contribution, and what you learned.

Job Type: Full-time

Pay: $3,843.54 - $10,603.56 per month

Work Location: In person