F1 ERS ML Design Optimizer

GMPPU; Concord (NC); On-site; USD 90,000 - 120,000; Full time

Job summary

GMPPU in Concord, NC is seeking an ERS ML Design Optimization Engineer to leverage machine learning for design optimization and performance analysis of ERS systems. The ideal candidate has a strong background in ML and engineering, with expertise in building neural surrogates and optimizing systems for F1.

The role requires a Bachelor's in a relevant field, and a Master's or PhD is preferred. Responsibilities include developing workflows integrated with simulation tools to enhance ERS design while ensuring high fidelity against real-world data.

Qualifications

• Bachelor's in a relevant field; Master's/PhD preferred.
• 3+ years of experience in building neural surrogates.
• Familiarity with hybrid powertrains and multi-physics sim tools.

Responsibilities

• Build neural network surrogates emulating ERS physics.
• Implement optimization loops for multi-objective ERS design.
• Fuse/process petabyte-scale datasets for validation.

Skills

Neural network surrogates

Multi-fidelity datasets handling

GA/BO optimization

Hybrid powertrains knowledge

Domain expertise in e-motors

Education

Bachelor's in CS/EE/Math/Physics

Master's/PhD in ML/scientific computing

Tools

NX

MATLAB

ANSYS

Job description

GMPPU in Concord, NC is seeking an ERS ML Design Optimization Engineer to leverage machine learning for design optimization and performance analysis of ERS systems. The ideal candidate has a strong background in ML and engineering, with expertise in building neural surrogates and optimizing systems for F1.

The role requires a Bachelor's in a relevant field, and a Master's or PhD is preferred. Responsibilities include developing workflows integrated with simulation tools to enhance ERS design while ensuring high fidelity against real-world data.