Machine Learning Engineer II - Learned Planning (Reinforcement Learning)

Location: Remote in the United States or hybrid office work in Ann Arbor, Michigan, United States.

Salary: $153,200–$183,800 USD per year, plus potential bonus, stock options, sign-on payments, and relocation compensation.

Company

hirify.global develops software for automated trucks and autonomous vehicle technology as part of the Daimler family.

What you will do

• Develop and train learned behavior models using behavior cloning, imitation learning, reinforcement learning, and sequence modeling.
• Implement production-quality Python and PyTorch code for model training, evaluation, and inference.
• Analyze model performance and failure modes to improve robustness and generalization across driving scenarios.
• Build training pipelines and curate behavior datasets from simulation, fleet logs, and on-vehicle data.
• Collaborate with perception, prediction, planning, simulation, validation, safety, and autonomy engineering teams.
• Integrate models into simulation and testing workflows and improve experimentation speed, reproducibility, and 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 imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
• Strong programming skills in Python and PyTorch, including production-quality machine learning code.
• Experience training and evaluating models with large datasets and scalable compute environments.
• Understanding of autonomy architectures such as transformers, graph neural networks, or sequence models.
• Experience debugging model behavior, analyzing metrics, iterating on training pipelines, and integrating ML models into larger software systems.

Nice to have

• Experience in autonomous driving, robotics, or simulation-based training environments.
• Experience with reinforcement learning frameworks or distributed training systems such as Ray.
• Experience with simulation environments, large-scale behavior datasets, vehicle dynamics, motion planning, or multi-agent decision-making.
• Experience deploying ML models into production or real-world robotics systems.

Culture & Benefits

• Collaborative, energetic, and team-focused work environment.
• Competitive compensation with bonus and stock options.
• 100% company-paid medical, dental, and vision premiums for full-time employees.
• 401(k) plan with a 6% employer match.
• Flexible scheduling, generous paid vacation available immediately after starting, AD&D insurance, and life insurance.