Expert Machine Learning Optimization Engineer - Self Driving
On-siteFrankfurt am Main, Hesse, Germany
Job Summary
Optimize machine learning models for perception, prediction, and planning stacks to run on real-time automotive hardware. Design efficient training and inference workflows, implementing techniques like quantization, pruning, and mixed-precision training. Profile CPU/GPU/accelerator performance to identify bottlenecks in runtime, memory usage, and throughput. Build distributed training pipelines using TensorRT, ONNX, and CUDA kernels to achieve low-latency deployment. Benchmark optimized models across diverse real-world and simulated driving scenarios to ensure robustness. Collaborate with system teams to integrate models into the full self-driving stack while maintaining automotive-grade safety and reliability standards.
Required Qualifications
- MSc/PhD in Computer Science, Electrical Engineering, Robotics, or a related field
- 5+ years of relevant industry experience
- Strong foundation in machine learning, deep learning, and computer vision
- Proficiency in Python and C++
- Hands-on experience with PyTorch or TensorFlow, including large-scale model training and optimization
- Expertise in deploying and optimizing models for embedded or real-time automotive systems using CUDA, TensorRT, ONNX, or equivalent frameworks
- Skilled in profiling and debugging ML code on GPUs and accelerators using profiler tools such as NVIDIA Nsight or similar
- Strong understanding of memory management, parallelization, and performance optimization on embedded processors
- Experience applying software engineering best practices (CI/CD, testing, version control) to ML pipelines
- Excellent problem-solving skills
- Ability to thrive in a fast-paced, collaborative team environment
Desired Qualifications
- Prior experience in autonomous driving or ADAS development
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