• Own the model roadmap for Road & Lane Detection within the Model Dev ML org — from concept through production-grade model maturity. • Research, design, and train advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion models, topological lane graph networks) to detect, segment, and model road structures and lane connectivity. • Lead data strategy for this domain — defining data curation, labeling policies, and active learning pipelines to capture long-tail scenarios (e.g., occlusions, complex merges, construction zones). • Develop robust metrics and evaluation frameworks for lane and road geometry accuracy, temporal consistency, and cross-domain generalization. • Advance foundational capabilities such as self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling for road and lane understanding. • Drive large-scale experiments — designing, running, and analyzing results from distributed training workflows and ablations to identify scalable improvements. • Collaborate with other model dev/perception teams to ensure model coherence and interface consistency. • Mentor engineers and scientists, setting best practices for model training, evaluation, and code quality. • Stay ahead of the research frontier by evaluating and adapting emerging techniques (e.g., BEV-based large models, vectorized map prediction, lane graph transformers) to production-grade perception.