Senior ML Engineer - Learned Localization
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Die Stelle
Line 1: Design, implement, and optimize ML models for localization, including learned pose estimation, map-matching, and sensor fusion using camera, LiDAR, and radar data.
Line 2: Build high-performance training and evaluation workflows with PyTorch, distributed training, and large-scale datasets.
Line 3: Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, meeting real-time constraints.
Line 4: Analyze failure cases, perform ablations, improve model robustness, and drive rigorous experimentation for production-level reliability.
Line 5: Contribute to system design, code reviews, best practices, and documentation across ML and autonomy teams.
Line 6: Work with multimodal data, ensuring scalable, maintainable, and production-ready ML solutions.
Line 2: Build high-performance training and evaluation workflows with PyTorch, distributed training, and large-scale datasets.
Line 3: Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, meeting real-time constraints.
Line 4: Analyze failure cases, perform ablations, improve model robustness, and drive rigorous experimentation for production-level reliability.
Line 5: Contribute to system design, code reviews, best practices, and documentation across ML and autonomy teams.
Line 6: Work with multimodal data, ensuring scalable, maintainable, and production-ready ML solutions.
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