Senior Software Engineer – ADAS
Indexed description
We’re hiring a mid‑level Software Engineer to develop production ADAS and autonomous driving functions in C++ and Python. If you’re passionate about building robust, high‑performance features that run on GPUs in real vehicles, we’d like to hear from you.
What You’ll Be Doing
- Design, implement, and maintain **C++ ADAS functions** for perception, prediction, and planning (e.g., lane keeping, ACC, AEB, traffic‑light and object handling) in a safety‑critical codebase.
- Integrate deep learning models into C++ pipelines: take models trained in Python (PyTorch or TensorFlow), export/convert them, and deploy them for real‑time inference on NVIDIA GPUs.
- Work with multi‑sensor data — cameras, radar, lidar — and implement sensor fusion, tracking, and decision‑making logic in C++.
- Build and extend testable, modular libraries and components, including interfaces to models, sensor drivers, and vehicle control.
- Profile, debug, and optimize C++ and CUDA code to meet strict latency and throughput targets.
- Contribute to tooling around data quality, automated evaluation, and regression tests for ADAS functions.
- Collaborate closely with ML researchers, systems engineers, and automotive partners to turn prototype algorithms into production‑ready C++ implementations.
- 4–8 years of professional software engineering experience, ideally in **ADAS, automotive, robotics, or real‑time systems**.
- Master’s or PhD degree in Computer Science or in Machine Learning
- Strong modern C++ (C++14/17 or later): templates, RAII, smart pointers, STL, and experience building large codebases.
- Solid Python skills for tooling, training scripts, and glue code between data pipelines and C++ components.
- Hands‑on experience training and using deep learning models (PyTorch or TensorFlow): designing experiments, tuning hyperparameters, working with large datasets, and debugging model behavior.
- Experience developing on Linux: build systems (CMake), debugging (gdb, sanitizers), profiling, and git‑based workflows in a CI/CD environment.
- Familiarity with one or more of:
- GPU programming and optimization (CUDA, TensorRT, cuDNN)
- Computer vision / perception (object detection, segmentation, multi‑object tracking)
- Robotics or autonomous systems (ROS/ROS2, ADAS features, simulation environments)
- Direct experience implementing **ADAS functions in C++**, such as lane keeping, adaptive cruise control, automatic emergency braking, or traffic‑sign/traffic‑light handling.
- Experience with camera calibration, sensor fusion, or multi‑camera perception systems.
- Knowledge of model optimization and deployment: quantization (INT8, FP8, 4‑bit), TensorRT‑LLM, ONNX Runtime, or similar frameworks.
- Background in training infrastructure: distributed training, experiment tracking, dataset versioning, hyperparameter optimization.
- Understanding of software quality practices for safety‑critical systems (code review, unit testing, static analysis; automotive standards knowledge is a plus) as well as open‑source contributions or published work in AI, robotics, or GPU computing.
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