Staff, ML Engineer - Road & Lane Detection
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仕事内容
Own the Road & Lane Detection model roadmap from concept to production-grade maturity, shaping perception capabilities for autonomous driving.
Research, design, and train advanced neural architectures (e.g., BEV transformers, LiDAR-vision fusion, topology-based lane networks) to detect and model road structures and lane connectivity.
Lead data strategy for this domain, defining curation, labeling policies, and active learning pipelines to cover long-tail scenarios.
Develop robust metrics and evaluation frameworks for lane/road geometry accuracy, temporal consistency, and cross-domain generalization.
Advance self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling to strengthen road and lane understanding.
Drive large-scale experiments, mentor teammates, and collaborate across model development and perception teams to ensure production-grade quality.
Research, design, and train advanced neural architectures (e.g., BEV transformers, LiDAR-vision fusion, topology-based lane networks) to detect and model road structures and lane connectivity.
Lead data strategy for this domain, defining curation, labeling policies, and active learning pipelines to cover long-tail scenarios.
Develop robust metrics and evaluation frameworks for lane/road geometry accuracy, temporal consistency, and cross-domain generalization.
Advance self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling to strengthen road and lane understanding.
Drive large-scale experiments, mentor teammates, and collaborate across model development and perception teams to ensure production-grade quality.
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