Senior, ML Engineer - Learned Localization
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The role
Design, build, and optimize ML models for localization, including learned pose estimation, map-based localization, and sensor fusion pipelines using camera, LiDAR, and radar data.
Develop high-performance training and evaluation workflows with PyTorch, distributed training, and large-scale multimodal datasets.
Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack with real-time constraints.
Analyze failure cases, run ablations, improve model robustness, and drive rigorous experimentation to production-level reliability.
Contribute to system design, code reviews, best practices, and documentation across ML and autonomy teams.
Qualifications include 6+ years (or 3+ with Master’s) of applied ML for AV/Robotics, strong 3D geometry, probabilistic estimation, and production-grade Python/C++ software engineering.
Develop high-performance training and evaluation workflows with PyTorch, distributed training, and large-scale multimodal datasets.
Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack with real-time constraints.
Analyze failure cases, run ablations, improve model robustness, and drive rigorous experimentation to production-level reliability.
Contribute to system design, code reviews, best practices, and documentation across ML and autonomy teams.
Qualifications include 6+ years (or 3+ with Master’s) of applied ML for AV/Robotics, strong 3D geometry, probabilistic estimation, and production-grade Python/C++ software engineering.
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