ML Engineer, II - End to End (E2E)

Meet the Team: 
As a Machine Learning Engineer II – End-to-End, you will help develop and deploy End-to-End models that power both perception and decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to End-to-End models that enable safe, efficient, and human-like driving in real-world freight operations. 
 
This role focuses on building, validating, and improving machine learning models and infrastructure that support End-to-End systems within the autonomy stack. 
 
What You’ll Do 

• Develop and train machine learning models for End-to-End percetion and planning, including approaches such as imitation learning and reinforcement learning. 

• Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack. 

• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios. 

• Contribute to model training pipelines and data workflows, curating datasets from simulation, fleet logs, and on-vehicle data. 

• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate End-to-End models across diverse driving environments. 

• Help integrate End-to-End models into simulation and testing workflows, enabling faster iteration and more comprehensive validation. 

• Support the development of tooling and infrastructure that improve experimentation speed, reproducibility, and model iteration. 

• Contribute to technical discussions around model architecture and training strategies within the team. 

 
 
 
 
What You’ll Need to Succeed 

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