Machine Learning Engineer, Model Optimization
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El puesto
Lead optimization of model training and real-time inference for perception systems in autonomous driving.
Co-design model architectures and hardware to reduce FLOPs and leverage sparse spatial-temporal data.
Tune inference pipelines across onboard (vehicle) and offboard (simulation) platforms to meet latency requirements.
Analyze complex, multi-component models on the critical path within onboard systems to ensure robustness.
Apply Python-based ML frameworks (PyTorch or JAX) with potential C++ involvement for production.
Collaborate across teams, publish insights, and contribute to scalable, data-driven optimization.
Co-design model architectures and hardware to reduce FLOPs and leverage sparse spatial-temporal data.
Tune inference pipelines across onboard (vehicle) and offboard (simulation) platforms to meet latency requirements.
Analyze complex, multi-component models on the critical path within onboard systems to ensure robustness.
Apply Python-based ML frameworks (PyTorch or JAX) with potential C++ involvement for production.
Collaborate across teams, publish insights, and contribute to scalable, data-driven optimization.
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