Senior Physical AI Engineer - Digital Twins & Multi-Physics

McKinsey; Charlotte (NC); On-site; USD 173,000 - 211,000; Full time

Job summary

McKinsey & Company in the United States seeks a highly capable engineer to apply deep expertise in physical sciences, computational engineering, machine learning, and physics-based modeling to help leading industrial clients tackle their most complex engineering challenges.

You will lead technical workstreams on client engagements, building multi-physics simulation frameworks and digital twins, developing surrogate models, applying physics-informed ML to real-world systems, deploying scalable

Qualifications

• Advanced graduate degree in computational mechanics, materials science, mechanical/aerospace/nuclear engineering, computational physics, or closely related field is required.
• Demonstrated ability to develop high-quality engineering or scientific code (e.g., Python, C++).
• Fluency with statistical sampling, machine learning, and data science techniques.
• Experience with simulation, ML/AI/data science is a plus.

Responsibilities

• Lead technical workstreams on client engagements, building multi-physics simulation frameworks and digital twins.
• Develop surrogate and machine learning models and apply physics-informed machine learning to real-world engineering systems.
• Deploy scalable production tools on HPC infrastructure and translate computational insights into strategic recommendations.

Skills

Python

C++

Statistical sampling

Machine learning

Data science

Pytorch

Simulation experience

Organizational skills

Analytical thinking

Reporting / presentations

Time management

Willingness to travel

Communication skills

Education

MS or PhD in computational mechanics / related field

Tools

Pytorch

Python

C++

Job description

McKinsey & Company in the United States seeks a highly capable engineer to apply deep expertise in physical sciences, computational engineering, machine learning, and physics-based modeling to help leading industrial clients tackle their most complex engineering challenges.

You will lead technical workstreams on client engagements, building multi-physics simulation frameworks and digital twins, developing surrogate models, applying physics-informed ML to real-world systems, deploying scalable