Ml Engineer, II - End To End (Ai Engineering)

TL;DR

Ml Engineer, II (Ai Engineering): Develops and deploys End-to-End models for perception and decision-making in autonomous trucks with an accent on imitation learning and reinforcement learning. Focus on integrating models into simulation and testing workflows, enabling faster iteration and comprehensive validation.

Location: Remote (US), Ann Arbor, MI, Montreal, Canada, Remote (Canada)

Company

hirify.global is developing End-to-End models that power both perception and decision-making for autonomous trucks.

What you will do

• Develop and train machine learning models for End-to-End perception and planning.
• Implement production-quality ML code to support model training, evaluation, and inference.
• Analyze model performance, identify failure modes, and propose improvements.
• Contribute to model training pipelines and data workflows.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate models.
• Support the development of tooling and infrastructure that improve experimentation speed and model iteration.

Requirements

• Bachelor’s degree in Computer Science, Robotics, or a related field with 4+ years of experience, or a Master’s degree with 2+ years of experience.
• Experience applying machine learning techniques to robotics, autonomous systems, or complex control environments.
• Strong programming skills in Python and PyTorch.
• Experience training and evaluating machine learning models using large datasets.
• Understanding of ML architectures used in End-to-End systems.
• Ability to collaborate with cross-functional teams to integrate ML models into larger software systems.

Nice to have

• Experience working in autonomous driving, robotics, or simulation-based training environments.
• Experience with reinforcement learning frameworks or distributed training systems.
• 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.

Culture & Benefits

• Work closely with teams across perception, prediction, planning, and safety.
• Contribute to End-to-End models that enable safe, efficient, and human-like driving.
• Focus on building, validating, and improving machine learning models and infrastructure.