Embedded ML Engineer

Aptiv

Krakow, Poland

In our team we design, develop, and validate systems to enhance the safety and comfort in the vehicle cabin. This includes the development of innovative software features and machine learning algorithms in driver state sensing, seat occupancy monitoring, child presence detection, or hand gesture recognition using 2D and 3D cameras as well as other interior sensing technologies. Driver state sensing plays an important role not only to prevent road accidents caused by drowsiness or distraction of the driver, but also in the context of higher levels of automated driving, e.g. hand over scenarios.

Your Role

In your daily job you will:

  • Deploying C/C++ machine learning applications on PC and embedded devices;
  • Writing high quality, tested and efficient software in C++ for our AI/ML SW Stack;
  • Performing neural network runtime optimization: quantization and pruning;
  • Optimizing scheduling of multiple neural network models across different device cores;
  • Working in an Agile SW development team (SCRUM) closely with Machine Learning Engineers, System Engineers, and SW engineers.

Your Background

Key ingredients for succeeding in the role are your:

  • Master's degree + 1 year of relevant experience or Bachelor’s degree + 2 years of relevant experience in computer science, electrical engineering or related field;
  • Strong software development skills in C++. You have a passion for clean, well-tested, and well-documented code, following an ASPICE development process;
  • Experience in integrating multiple SW modules into one system. Solid understanding of process scheduling, threading, pipelining, resource allocation;
  • Knowledge in compute HW architectures: CPU, GPU, DSP, NPU, SoC, AI accelerators;
  • Fluent in English. Excellent written and verbal communication skills & interpersonal skills;
  • Practical experience with Linux, version control systems (Git), build systems (Make, CMake, Autotools) and code review tools (Gerrit, Gitlab, Bitbucket).

Nice to have:

  • Practical experience with one or more neural network inference engines: ONNX Runtime, TensorRT, TFLite;
  • Solid understanding of artificial intelligence, model building and evaluation;
  • Solid knowledge in at least one common ML framework (PyTorch, Tensorflow).

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