Patrick Hart
Senior Autopilot Machine Learning Engineer at Tesla
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolPatrick Hart is a Senior Autopilot Machine Learning Engineer with nine years of experience designing motion planning and reinforcement learning systems for autonomous vehicles. Currently at Tesla after a senior motion planning role at Argo AI, he blends academic rigor—pursuing a PhD at TUM—with hands-on production work shipping complex autonomy stacks. His background in mechanical engineering and thesis on kinodynamic planning informs a rare cross-domain fluency between vehicle dynamics and ML-driven decision making. Patrick has collaborated with major industry labs (Audi, Intel Labs, AID) on research-to-product pipelines, and maintains a public portfolio of his work at patrickhart.github.io that highlights practical implementations beyond typical academic papers.
9 years of coding experience
6 years of employment as a software developer
Master’s Degree, Mechanical Engineering, Master’s Degree, Mechanical Engineering at Karlsruhe Institute of Technology (KIT)Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Technical University of MunichStudy abroad (one year), Study abroad (one year) at University of ConnecticutDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at TUM Graduate School
English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (40)
simulation10
algorithms10
benchmark10
artificial-intelligence10
robotics10
simulator10
gnn10
openai-gym10
machine-learning10
verification10
multi-agent10
benchmarking10
reinforcement-learning-environments10
reinforcement-learning10
autonomous-vehicles10
Programming languages (2)
Github contributions (5)
bark-simulator/bark
Apr 2019 - Jan 2022
Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving
Contributions:1 release, 124 reviews, 1083 commits in 2 years 10 months
autonomous-drivingbenchmarkingsimulationbenchmarkmulti-agent
bark-simulator/bark-ml
Sep 2019 - Feb 2022
Gym environments and agents for autonomous driving.
Contributions:5 reviews, 1060 commits, 196 PRs in 2 years 5 months
autonomous-drivingreinforcementagentsreinforcement-learning-environmentsgym-environments
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