Expert Machine Learning Optimization Engineer - Self Driving

VinFast; Frankfurt; Vor Ort; EUR 85.000 - 120.000; Vollzeit

Zusammenfassung

A leading electric vehicle company is seeking an Expert Machine Learning Optimization Engineer to enhance self-driving models. Located in Frankfurt, you'll collaborate with teams to optimize models for embedded automotive systems using advanced ML techniques. Ideal candidates will have a strong ML background with expertise in Python and C++, alongside experience in real-time systems. This role presents an opportunity to work with industry leaders and innovate in sustainable mobility solutions.

Qualifikationen

• 5+ years of relevant industry experience in ML.
• Expertise in deploying models for real-time automotive systems.
• Strong understanding of memory management and performance optimization.

Aufgaben

• Optimize ML models for self-driving vehicles on embedded processors.
• Develop and integrate inference pipelines for low-latency performance.
• Benchmark and validate models against real-world driving scenarios.

Kenntnisse

Machine learning

Deep learning

Computer vision

Python

C++

PyTorch

TensorFlow

CUDA

TensorRT

Profiling/debugging ML code

Ausbildung

MS or PhD in Computer Science, Electrical Engineering, Robotics

Tools

TensorRT

ONNX

NVIDIA Nsight

Jobbeschreibung

Expert Machine Learning Optimization Engineer - Self Driving

Frankfurt am Main, Germany | Posted on 11/06/2025

VINFAST is a pioneering electric vehicle (EV) company committed to revolutionizing the automotive industry with sustainable and innovative mobility solutions. As a leading player in the EV market, VinFast is dedicated to delivering high-quality, cutting-edge electric vehicles that redefine the driving experience. Our team consists of passionate professionals driven by a shared vision of creating a greener and more sustainable future through innovation, technology, and excellence.

We are seeking a Senior Machine Learning Optimization Engineer to accelerate and optimize the machine learning models powering our self-driving stack. In this role, you will focus on bringing cutting-edge perception, prediction, and planning models to life on real-time automotive hardware. You will design and implement efficient training and inference workflows, ensuring that large-scale models run with the robustness, latency, and efficiency required for safe deployment in autonomous vehicles.

• Collaborate with perception, prediction, planning and other teams to optimize models for deployment on embedded automotive processors (e.g., NVIDIA A, Qualcomm, TI)
• Profile and analyze CPU/GPU/accelerator performance to identify bottlenecks in runtime, memory usage, and throughput
• Implement and integrate model optimization techniques including quantization, pruning, distillation, mixed‑precision training, and efficient neural architectures
• Build and optimize inference pipelines using TensorRT, ONNX, CUDA kernels, and other compilation frameworks to achieve low‑latency, real‑time performance
• Develop distributed training and scalable optimization pipelines to support large‑scale training of self‑driving models
• Benchmark and validate optimized models across diverse real‑world and simulated driving scenarios, ensuring robustness under edge cases
• Collaborate with system teams to integrate optimized models into the full self‑driving stack
• Contribute to CI/CD, build systems, and automated testing pipelines for deployment of optimized ML models
• Ensure compliance with automotive‑grade safety, reliability, and performance requirements

Requirements

• MS or PhD in Computer Science, Electrical Engineering, Robotics, or a related field with 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 and the ability to thrive in a fast‑paced, collaborative team environment
• Nice to have: Prior experience in autonomous driving or ADAS development
• Opportunity to collaborate with and learn from industry‑leading professionals in the automotive domain.

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