At a Glance
- Tasks: Leverage ML for design optimisation and performance analysis of cutting-edge ERS systems.
- Company: Join GM Performance Power Units, a leader in innovative automotive engineering.
- Benefits: Competitive salary, inclusive culture, and opportunities to work on F1 technology.
- Other info: Dynamic team environment with a focus on innovation and career growth.
- Why this job: Make a real impact on next-gen Formula 1 power units and push the boundaries of performance.
- 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.
ERS ML Design Optimization Engineer employer: GM Performance Power Units
At GM Performance Power Units in Concord, NC, we pride ourselves on fostering a culture of innovation and precision, where your contributions directly impact the future of Formula 1 power units. We offer competitive benefits, a collaborative work environment, and ample opportunities for professional growth, ensuring that our employees are not only part of a team but also part of a legacy of performance excellence.
Contact Details:
GM Performance Power Units Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land ERS ML Design Optimization Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at GM Performance Power Units on LinkedIn. A friendly chat can give us insights into the company culture and maybe even a referral!
✨Tip Number 2
Prepare for the interview by brushing up on your ML knowledge and how it applies to ERS systems. We want to show that we can talk the talk and walk the walk when it comes to design optimisation.
✨Tip Number 3
Practice common interview questions, especially those related to teamwork and problem-solving. We need to demonstrate our soft skills alongside our technical expertise!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows we’re serious about joining the team!
We think you need these skills to ace ERS ML Design Optimization Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the ERS ML Design Optimization Engineer role. Highlight your experience with neural networks and optimisation techniques, and don’t forget to mention any relevant projects that showcase your skills in ML and engineering simulations.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about F1 performance engineering and how your background aligns with the job. Be sure to mention specific experiences that demonstrate your ability to innovate and deliver under pressure.
Showcase Your Soft Skills:While technical skills are crucial, don’t overlook the importance of soft skills. We want to see how you communicate complex ideas and collaborate with design engineers. Share examples of how you've successfully worked in teams or tackled challenges in past roles.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details you need about the role and our company culture there!
How to prepare for a job interview at GM Performance Power Units
✨Know Your ML Stuff
Make sure you brush up on your machine learning knowledge, especially around neural networks and surrogate models. Be ready to discuss your experience with PyTorch or TensorFlow, as well as any specific projects where you've built neural surrogates for engineering simulations.
✨Understand the Role
Dive deep into the specifics of the ERS systems mentioned in the job description. Familiarise yourself with terms like MGU-K, CU-K, and ES, and be prepared to explain how your skills can contribute to optimising these systems under FIA constraints.
✨Showcase Your Problem-Solving Skills
Prepare examples that highlight your ability to tackle complex problems, especially in multi-objective optimisation scenarios. Think about times when you've had to balance trade-offs between mass, power, and reliability in your designs.
✨Communicate Clearly
Since you'll need to convey complex ML insights to design engineers, practice explaining your past projects in simple terms. Being able to communicate effectively will show that you can bridge the gap between technical and non-technical team members.
