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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Systems Software Engineer - **Company:** microTECH Global Ltd - **Location:** München, Germany - **Experience:** Experienced - **Contract:** Temporary contract - **Skills:** Artificial Neural Networks, Computer Vision, C++ (Programming Language), Python (Programming Language), Software Architecture, ISO/IEC 15504, Software Engineering, Systems Architecture, Pytorch, Deep Learning, Low Latency, Machine Learning Operations, TensorRT, Edge Detection, GPT - **Published:** August 21, 2026 - **Apply:** https://www.jobfinder.de/job/system-software-engineer/ ## About the Role ![](https://www.jobfinder.de/wp-content/uploads/2023/11/bax-logo.png) Systems Software Engineer Work Location: Munich Experience: 4-5 years Contract duration: 12 months Working arrangement: 3 days per week in the Munich office, 2 days remote Role Summary Responsible for designing, implementing, and validating a camera-based lane detection and tracking system for an Advanced Driver Assistance System (ADAS). The role spans the full pipeline: image preprocessing, neural-network-based lane/marking detection, temporal tracking, lane geometry modeling, and vehicle-state fusion for warning logic. Key Responsibilities - Analyze requirements and define the system architecture for a front-camera-based lane detection function (inputs, outputs, latency/accuracy targets, ODD - operational design domain). - Design and train a neural network (e.g., segmentation-based, anchor-based, or row-classification-based architectures such as LaneNet, SCNN, UFLD, PolyLaneNet, or transformer-based approaches) for lane marking/lane boundary detection. - Implement lane tracking across frames (Kalman filter, particle filter, or learned temporal models) to ensure stable, jitter-free lane estimates and handle occlusion, worn markings, or missing lanes. - Fit and maintain a lane geometry model (e.g., clothoid/polynomial curve fitting) and estimate vehicle position/heading relative to the lane. - Develop the Lane Departure Warning logic: time-to-lane-crossing (TTLC) estimation, threshold logic, driver intent filtering (e.g., turn signal suppression), and warning triggering strategy. - Integrate camera calibration (intrinsic/extrinsic) and perspective transformation (IPM - inverse perspective mapping) into the pipeline. - Optimize models for embedded/automotive-grade hardware (quantization, pruning, TensorRT/embedded inference frameworks) to meet real-time constraints. - Build datasets, define annotation guidelines, and drive data collection strategy for diverse conditions (rain, night, glare, worn markings, construction zones, curves). - Validate against relevant standards (e.g., Euro NCAP LDW/LKA test protocols) and define test/validation KPIs (false positive/negative rates, detection range, curvature accuracy). - Collaborate with vehicle integration teams; support HIL/vehicle-level testing. Required Skills & Experience - Strong background in computer vision and deep learning, especially semantic segmentation, keypoint detection, or curve-fitting-based lane detection architectures. - Proficiency in Python and deep learning frameworks (PyTorch). - Solid understanding of classical CV techniques: camera calibration, homography/IPM, edge detection, Hough transforms - useful for hybrid approaches and sanity baselines. - Experience with object/lane tracking algorithms (Kalman filter, EKF, particle filters) and sensor/temporal fusion. - Familiarity with curve/polynomial or clothoid-based lane modeling. - Experience deploying models on embedded/automotive compute - C++ proficiency for production/embedded implementation. ADAS/domain-specific: - Understanding of ADAS software architecture and real-time constraints. - Familiarity with automotive standards: Euro NCAP test protocols for LDW/LKA, ASPICE process awareness. - Experience with lane detection datasets (e.g., TuSimple, CULane, BDD100K, or proprietary OEM datasets). Tid-1 ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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