Machine Learning Engineer - Perception Offline Driving Intelligence
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The role
Develop and fine-tune multimodal large language models to enhance environmental understanding for autonomous driving, with a focus on offline analysis.
Design model architectures and training techniques, build high-quality datasets from sensor data, and drive ML work from research to production.
Collaborate with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline with low latency.
Validate solutions using real-world driving scenarios to improve safety, reliability, and overall robotaxi performance.
Leverage Python, PyTorch, and NumPy to implement data preprocessing, training pipelines, and evaluation on large-scale data.
Operate in a fast-moving, collaborative environment to advance off-vehicle analysis and multimodal understanding for urban autonomy.
Design model architectures and training techniques, build high-quality datasets from sensor data, and drive ML work from research to production.
Collaborate with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline with low latency.
Validate solutions using real-world driving scenarios to improve safety, reliability, and overall robotaxi performance.
Leverage Python, PyTorch, and NumPy to implement data preprocessing, training pipelines, and evaluation on large-scale data.
Operate in a fast-moving, collaborative environment to advance off-vehicle analysis and multimodal understanding for urban autonomy.
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