Staff, ML Engineer - Road & Lane Detection
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El puesto
Own the model roadmap for Road & Lane Detection within the Model Dev ML org, driving concept-to-production maturity.
Research and design advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion, topological lane graphs) to model road structures and lane connectivity.
Lead data strategy for this domain, defining curation, labeling policies, and active learning pipelines to capture long-tail scenarios.
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 and ablations.
Mentor engineers, collaborate with other model dev teams for coherence, and stay ahead of research frontiers in production-grade perception.
Research and design advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion, topological lane graphs) to model road structures and lane connectivity.
Lead data strategy for this domain, defining curation, labeling policies, and active learning pipelines to capture long-tail scenarios.
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 and ablations.
Mentor engineers, collaborate with other model dev teams for coherence, and stay ahead of research frontiers in production-grade perception.
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