ERS ML Design Optimization Engineer
GM Performance Power Units, Concord, NC, United States
GM Performance Power Units - Concord, NC
ERS ML Design Optimization Engineer - Onsite
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 simulation 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 and uncertainty quantification on ERS parameter spaces.
Develop ML‑accelerated workflows integrated with NX/AVL/MATLAB/ANSYS simulation 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 simulations; 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 simulation tools.
Desirable Skills
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 simulation fidelity against reality.
Passionate about F1 performance engineering.
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.
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ERS ML Design Optimization Engineer - Onsite
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 simulation 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 and uncertainty quantification on ERS parameter spaces.
Develop ML‑accelerated workflows integrated with NX/AVL/MATLAB/ANSYS simulation 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 simulations; 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 simulation tools.
Desirable Skills
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 simulation fidelity against reality.
Passionate about F1 performance engineering.
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.
#J-18808-Ljbffr