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
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience.
- Experience applying machine learning techniques such as computer vision, imitation learning, or reinforcement learning, to robotics, autonomous systems, or complex control environments.
- Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
- Experience training and evaluating machine learning models using large datasets and scalable compute environments.
- Understanding of ML architectures used in End-to-End systems, such as BEV models, Transformers, VLA, or diffusion models.
- Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
- Ability to collaborate with cross-functional teams to integrate ML models into larger software systems.
Bonus Points!
- Experience working in autonomous driving, robotics, or simulation-based training environments.
- Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray).
- Experience with VLA or Neural Rendering.
- Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems.
- Experience deploying ML models into production or real-world robotics systems.