At a Glance
- Tasks: Leverage ML for design optimisation and performance analysis of Formula 1 ERS systems.
- Company: Join GM Performance Power Units, a leader in innovative automotive engineering.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Other info: Passionate team focused on precision and innovation in motorsport engineering.
- Why this job: Be at the forefront of F1 technology and make a real impact on performance engineering.
- Qualifications: Bachelor's in relevant field; experience with neural networks and optimisation required.
The predicted salary is between 60000 - 80000 £ per year.
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 behaviours.
- 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.
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 sim 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.
Formula 1 ERS ML Design Optimization Engineer employer: GM Performance Power Units
At GM Performance Power Units in Concord, NC, we pride ourselves on fostering a dynamic work environment that champions innovation and precision in the fast-paced world of Formula 1 engineering. Our commitment to employee growth is evident through continuous learning opportunities and collaborative projects that push the boundaries of technology. Join us to be part of a team where your contributions directly impact the performance of next-generation power units, all while enjoying a culture that values diversity and encourages every voice to be heard.
Contact Details:
GM Performance Power Units Recruitment Team
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We think this is how you could land Formula 1 ERS ML Design Optimization Engineer
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We think you need these skills to ace Formula 1 ERS ML Design Optimization Engineer
Some tips for your application 🫡
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✨Brush Up on Core Engineering Principles
Before heading into the interview with GM Performance Power Units, make sure you're solid on the fundamental engineering principles relevant to the role. Be ready to discuss concepts such as thermodynamics, fluid mechanics, or structural analysis, depending on the specifics mentioned in the job description. Don’t skip any hands-on projects or coursework; these can be excellent talking points!
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Expect technical questions or case studies during your interview—after all, engineering is all about solving problems! Prepare a few examples of how you've tackled engineering challenges in the past, whether at university or in any practical experience. Practising with mock technical interviews can really help you articulate your thought process and solutions.
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