Phillip Mueller
AI Solutions Engineer at BMW Group
Munich, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolPhillip Mueller is an AI Solutions Engineer with nine years of experience bridging computational engineering, aerospace, and industrial R&D, currently applying visual generative models at BMW Group in Munich. He holds a PhD candidacy in Computational Engineering and a strong mechanical engineering background, with hands-on projects in CFD, hybrid-electric aircraft powertrains, and generative design. Phillip combines academic rigor with production-focused delivery from internships and dual-study roles at MTU and extensive internal roles at BMW, moving ideas from simulation to application. He has contributed to open-source AutoML tooling by documenting hyperparameter optimization workflows for RNNs in hpbandster, reflecting practical ML deployment experience beyond theory. Known for curiosity across AI, robotics, and digitalization, he thrives at the intersection of simulation, data-driven models, and engineering design.
9 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Mechanical Engineering, Master of Science - MS, Mechanical Engineering at Brandenburgische Technische Universität Cottbus-SenftenbergBachelor of Engineering - BE, Mechanical Engineering, 1,4 (German grading system), Bachelor of Engineering - BE, Mechanical Engineering, 1,4 (German grading system) at Berlin School of Economics and LawDoctor of Philosophy - PhD, Computational Engineering, Doctor of Philosophy - PhD, Computational Engineering at University of Augsburg
Github Skills (14)
hyperparameter-optimization10
pytorch10
rnn-model10
automated-machine-learning10
n10
python9
machine-learning9
documentation8
optimization8
optimisation8
text-classification8
optmization8
neural-architecture-search7
automl7
Programming languages (2)
Github contributions (5)
automl/HpBandSter
Jul 2018 - Nov 2018
a distributed Hyperband implementation on Steroids
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 14 pushes in 4 months
Contributions summary:Phillip primarily contributed to the documentation of the project, adding examples and clarifying the usage of the provided functions. The contributions involved documenting code related to a Recurrent Neural Network (RNN) applied to the 20 Newsgroups dataset within the `hpbandster` framework. Their work showcases an understanding of how to apply hyperparameter optimization within the context of a PyTorch-based machine learning model for text classification.
automated-machine-learningneural-architecture-searchhyperbandmultiobjective-optimizationhyperparameters
PhMueller/HPOlib2
Jun 2020 - Jan 2021
Contributions:158 pushes, 56 branches in 7 months
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