ERS ML Design Optimization Engineer

Motorsport Network, LLC; Concord (NC); Hybrid; USD 110,000 - 165,000; Full time

Benefits offered by this job

Bonus scheme

Private healthcare

401(k) plan with company match

Employer-paid life insurance

Disability coverage

32 days annual leave

On-site gym

GM vehicle purchase/lease program

Free on-site EV charging

Job summary

GM Performance Power Units in Concord, NC seeks an ERS ML Design Optimization Engineer to advance ML-driven design optimization for ERS systems, leveraging surrogate models to accelerate cycles while meeting FIA constraints.

The role emphasizes building neural surrogates, performing multi-objective optimization, and integrating ML workflows with NX/AVL/MATLAB/ANSYS toolchains to support real-world race applications.

Qualifications

• Bachelor's in CS/EE/Math/Physics; Master's/PhD in ML/scientific computing preferred.
• 3+ years building neural surrogates for engineering sims; GA/BO optimization experience.
• Expert in PyTorch/TensorFlow/JAX; large-scale time-series/physics data pipelines.
• Proficiency handling multi-fidelity datasets (real + DiL/HiL/SiL).
• Familiarity with hybrid powertrains, multi-physics sim tools.

Responsibilities

• Build neural network surrogates (e.g., PINNs, graph nets) emulating ERS physics across thermal, electrical, degradation behaviors.
• Implement tool-agnostic GA/BO optimization loops for multi-objective ERS design (mass/power/reliability).
• Fuse/process petabyte-scale datasets from bench/dyno/track + DiL/HiL/SiL sims for training/validation.
• Conduct sensitivity analysis, uncertainty quantification on ERS parameter spaces.
• Develop ML-accelerated workflows integrated with NX/AVL/MATLAB/ANSYS sim chains.
• Validate models against real duty cycles; iterate for FIA-constrained optima.
• Document optimization pipelines, neural architectures, and results for design reviews.

Skills

Neural surrogates

GA/BO optimization

ML frameworks

Time-series data

Multi-physics data

Education

Bachelor's in CS/EE/Math/Physics

Master's/PhD preferred

Tools

PyTorch

TensorFlow

JAX

NX

AVL

MATLAB

ANSYS

Job description

GM Performance Power Units - Concord, NC
ERS ML Design Optimization Engineer - Onsite
Job Summary
GM Performance Power Units (GM PPU) seeks an ERS ML Design Optimization Engineer to join our team in Concord, NC. This role leverages ML for design optimization, simulation acceleration, and performance analysis of ERS systems (MGU-K, CU-K, ES) using telemetry and physics-based data. Focus on surrogate models to reduce sim cycles while meeting FIA constraints.

Key Responsibilities

• Build neural network surrogates (e.g., PINNs, graph nets) emulating ERS physics across thermal, electrical, degradation behaviors.
• Implement tool-agnostic GA/BO optimization loops for multi-objective ERS design (mass/power/reliability).
• Fuse/process petabyte-scale datasets from bench/dyno/track + DiL/HiL/SiL sims for training/validation.
• Conduct sensitivity analysis, uncertainty quantification on ERS parameter spaces.
• Develop ML-accelerated workflows integrated with NX/AVL/MATLAB/ANSYS sim chains.
• Validate models against real duty cycles; iterate for FIA-constrained optima.
• Document optimization pipelines, neural architectures, and results for design reviews.

Required Qualifications

• Bachelor's in CS/EE/Math/Physics; Master's/PhD in ML/scientific computing preferred.
• 3+ years building neural surrogates for engineering sims; GA/BO optimization experience.
• Expert in PyTorch/TensorFlow/JAX; large-scale time-series/physics data pipelines.
• Proficiency handling multi-fidelity datasets (real + DiL/HiL/SiL).
• Familiarity with hybrid powertrains, multi-physics sim tools.

Preferred Qualifications

• F1 ERS plant modeling (cell/MGU/ES performance prediction).
• Neural operators/PINNs for PDE surrogates; multi-fidelity BO.
• HPC workflows, data versioning (DVC), containerization.
• Domain expertise in e-motors, batteries, power electronics.

Personal Attributes

• Delivers under aggressive development timelines.
• Innovates across model/design/compute trade-offs.
• Communicates complex ML insights to design engineers.
• Rigorous validator of sim fidelity against reality.
• Passionate about F1 performance engineering.

Ready to be part of a team that pushes boundaries both on and off the track? At GM Performance Power Units, we are developing a new era of performance for the Formula 1 grid. Joining us means entering a startup racing environment backed by a legacy of engineering excellence, giving you the rare opportunity to help build a world-class program from day one. We believe in rewarding innovation and powering potential. As part of our team, you'll benefit from a competitive and comprehensive package designed to support your performance, well-being, and future, helping you thrive at every level.

That includes access to:

• Bonus scheme
• Private healthcare
• 401(k) plan including company match
• Employer-paid life insurance and disability coverage
• 32 days annual leave (including holidays)
• Free financial advisor access
• Employee Assistance Programs (EAP)
• Subsidized on-site dining
• On-site gym
• Discounted GM vehicle purchase/lease program
• Free on-site EV charging for personal GM vehicles

It's all part of our commitment to creating a workplace that’s as high-performing and forward-thinking as the people who make it succeed.

Join us and help power what's next.

Why Join Us

You'll play a pivotal role in ensuring the reliability and performance of a next-generation Formula 1 power unit. Our culture rewards precision, innovation, and the relentless pursuit of performance.
Please note: GM Performance Power Units and all affiliated companies are Equal Opportunity employer(s). Minorities, women, veterans, and individuals with disabilities are encouraged to apply. For more information regarding the EEOC, please visit https://www.eeoc.gov/employers/upload/poster_screen_reader_optimized.pdf.
Only direct hires need apply to or inquire about job postings at GM Performance Power Units. We are not accepting calls, resumes or applications from recruiting firms at this time.