Machine Learning Engineer, Model Optimization
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The role
As a Machine Learning Engineer, you will optimize FLOPs usage in training and inference through architecture–hardware co-development, focusing on sparse representations common in perception tasks.
You will optimize model inference across onboard and offboard (simulation) platforms, ensuring efficient performance in real-time settings.
You will analyze multi-component model architectures to streamline the critical paths in onboard systems.
You will work with Python and ML frameworks (PyTorch or JAX) to develop scalable training pipelines and deployable inference solutions.
Preferred candidates have 3+ years in ML/Computer Vision, a strong publication record, and experience with C++.
This hybrid role reports to a Technical Lead Manager and emphasizes collaboration, innovation, and impact on autonomous perception systems.
You will optimize model inference across onboard and offboard (simulation) platforms, ensuring efficient performance in real-time settings.
You will analyze multi-component model architectures to streamline the critical paths in onboard systems.
You will work with Python and ML frameworks (PyTorch or JAX) to develop scalable training pipelines and deployable inference solutions.
Preferred candidates have 3+ years in ML/Computer Vision, a strong publication record, and experience with C++.
This hybrid role reports to a Technical Lead Manager and emphasizes collaboration, innovation, and impact on autonomous perception systems.
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