System Software Engineer

microTECH Global LTD; München; Hybrid; EUR 75.000 - 110.000; Vollzeit

Zusammenfassung

microTECH Global LTD in Munich seeks an engineer to design, implement, and validate a camera-based lane detection system for ADAS. You will manage the full pipeline from image preprocessing to neural-network-based detection and temporal tracking.

Responsibilities include lane geometry modeling, TTLC warnings, camera calibration integration, and real-time optimization for embedded hardware. The role requires strong CV and Python/PyTorch skills, plus C++ for production deployment, with a hybrid

Qualifikationen

• Strong background in computer vision and deep learning for lane detection.
• Proficiency in Python and PyTorch.
• Familiarity with calibration, IPM, and CV techniques.
• Experience with embedded/ automotive compute deployment.
• C++ proficiency for production/embedded implementation.

Aufgaben

• Define the system architecture for front-camera lane detection.
• Design and train neural networks for lane boundary detection.
• Implement lane tracking across frames with Kalman or particle filters.
• Fit lane geometry and estimate vehicle position relative to the lane.
• Develop lane departure warning logic and TTLC estimation.
• Integrate camera calibration and IPM into the pipeline.
• Optimize models for real-time embedded hardware.
• Build datasets and define annotation guidelines for diverse conditions.
• Validate against Euro NCAP LDW/LKA test protocols.
• Collaborate with vehicle integration teams for HIL testing.

Kenntnisse

Computer vision

Deep learning

Python

PyTorch

Kalman filter

Lane detection

Image preprocessing

C++

Embedded deployment

Calibration

HIL testing

Tools

TensorRT

CUDA

OpenCV

ONNX

Jobbeschreibung

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).