Ml Engineer, II - Learned Behaviors (Ai)

TL;DR

Ml Engineer, II - Learned Behaviors (Ai): Develop and deploy behavior models that power decision-making for autonomous trucks with an accent on building, validating, and improving machine learning models and infrastructure. Focus on integrating learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.

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

Company

hirify.global is developing behavior models that power decision-making for autonomous trucks.

What you will do

• Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, 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 behavior datasets from simulation, fleet logs, and on-vehicle data.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
• Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration.

Requirements

• 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 imitation learning, reinforcement learning, or sequence modeling 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 autonomy systems, such as transformers, graph neural networks, or sequence models.
• 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 (e.g., Ray).
• Experience working with simulation environments or large-scale behavior datasets.
• Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems.
• Experience deploying ML models into production or real-world robotics systems.