Machine Learning Engineer

• Own the model roadmap for road and lane detection within the perception machine learning organization, from concept to 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 roadway structures and lane connectivity.
• Lead the data strategy for this domain: define 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.
• Run large-scale experiments: design, execute, and analyze distributed training workflows and ablation studies to identify scalable improvements.
• Collaborate with other perception teams to ensure model and interface consistency.
• Mentor engineers and scientists, defining best practices for training, model evaluation, and code quality.
• Stay at the cutting edge of research by evaluating and adapting emerging techniques (e.g., large BEV-based models, vectorized map prediction, lane-graph transformers) to production-grade perception.

Staff Machine Learning Engineer – Machine Learning, Road and Lane Detection

Senior Engineer – Machine Learning, Offline Perception

Staff Machine Learning Engineer – E2E

Staff ML Engineer – E2E

Staff ML Engineer – Learned Behaviors

Staff ML Engineer – Road & Lane Detection