Robotics AI Engineer (Deep Learning for Autonomy)

ROZOR; Hamilton; On-site; CAD 100,000 - 140,000; Full time

Job summary

ROZOR is seeking an Robotics AI Engineer (Deep Learning for Autonomy) in Hamilton, Ontario, Canada. You will design, train, and deploy neural models powering autonomy behaviors in ROZOR robots, enabling obstacle prediction, semantic mapping, scene understanding, and learning-based navigation.

The role involves developing DL for obstacle detection, behavior forecasting, semantic mapping, and multi-modal data processing, with pipelines for training and validation, and ROS 2 integration.

Qualifications

• Bachelor's or Master's in AI, Robotics, Computer Science or related field.
• Deep learning, computer vision, or reinforcement learning expertise.
• Proficiency with PyTorch or TensorFlow.
• Experience with time-series, trajectory data, or sequential prediction.
• Knowledge of robot navigation, SLAM, and mapping.
• Experience in detection, tracking, segmentation, or prediction training.

Responsibilities

• Develop deep learning for dynamic obstacle detection and motion prediction.
• Work on behavior forecasting in crowded environments.
• Handle semantic mapping and spatial understanding.
• Create learning-based navigation and path optimization.
• Process multi-modal data: RGB, LiDAR, depth, IMU, maps, and trajectories.
• Build training and validation pipelines.
• Integrate models into ROS 2-based autonomy stacks.
• Collaborate across planning, perception, and embedded teams.
• Conduct field testing and failure analysis.

Skills

Deep learning

Computer vision

Reinforcement learning

Time-series data

Debugging/analysis

Education

Bachelor's/Master's in AI/Robotics/CS

Tools

PyTorch

TensorFlow

Job description

Robotics AI Engineer (Deep Learning for Autonomy)

Hamilton, ON · Canada Full Time AI team

You will design, train, and deploy neural models that power high-level autonomy behaviors in ROZOR robots, obstacle prediction, semantic mapping, scene understanding, behavior forecasting, and learning-based navigation.

Responsibilities

• Develop deep learning for dynamic obstacle detection and motion prediction.
• Work on behavior forecasting in crowded environments.
• Handle semantic mapping and spatial understanding.
• Create learning-based navigation and path optimization.
• Process multi-modal data: RGB, LiDAR, depth, IMU, maps, and trajectories.
• Build training and validation pipelines.
• Integrate models into ROS 2-based autonomy stacks.
• Collaborate across planning, perception, and embedded teams.
• Conduct field testing and failure analysis.
• Support dataset generation and ML operations.

What you bring

• Bachelor's/Master's in AI, Robotics, Computer Science, or related field.
• Deep learning, computer vision, or reinforcement learning expertise.
• PyTorch or TensorFlow proficiency.
• Experience with time-series, trajectory data, or sequential prediction models.
• Robot navigation, SLAM, mapping knowledge.
• Detection, tracking, segmentation, or prediction training experience.
• Strong debugging and analytical abilities.