Systems Software Engineer

microTECH Global Limited; München; Hybrid; EUR 90.000 - 110.000; Vollzeit

Zusammenfassung

microTECH Global Limited in Munich seeks a Systems Software Engineer to design, implement, and validate a camera-based lane detection and tracking system for an ADAS. You will work across the full pipeline from image preprocessing to vehicle-state fusion, focusing on real-time performance.

The role demands expertise in computer vision and deep learning, Python (PyTorch), and C++ for embedded deployment. You will collaborate with cross-functional teams and support HIL testing in a 12-month,

Qualifikationen

• Strong background in computer vision and deep learning for lane detection.
• Proficiency in Python and PyTorch.
• Knowledge of camera calibration, IPM, edge detection, and Hough transforms.
• Experience with Kalman/particle filters and temporal fusion.
• C++ production/embedded experience.

Aufgaben

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

Kenntnisse

Computer vision

Deep learning

Lane detection

Python

PyTorch

C++

Tools

TensorRT

Jobbeschreibung

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).