Robotics AI Engineer — Edge Deployment & Data Quality

MARSO Robotics; Paris; On-site; EUR 70,000 - 110,000; Full time

Job summary

MARSO Robotics is seeking an AI Engineer to own the full lifecycle of embedded AI models for the Marso robot, from data quality and training to production reliability and tooling that speeds up development-to-deployment.

You will contribute to developing, training, and evaluating models, ensure reliability across PoCs, field tests and production, and build data pipelines and internal tools to monitor model performance.

Qualifications

• 3 to 5 years of experience in AI/ML engineering, ideally with an embedded or robotics component.
• Strong command of Python and deep learning frameworks (PyTorch, TensorFlow, or equivalent).
• Hands-on experience deploying models to production, including under hardware constraints (edge, quantization, optimization).
• Solid understanding of data pipelines: collection, cleaning, labeling, and quality measurement.
• Rigorous and autonomous, and you enjoy testing in real-world conditions, not just in simulation.
• Nice to have: experience with mobile robotics, ROS/ROS2, computer vision, or sensors (lidar, cameras, IMU).

Responsibilities

• Contribute to the development, training, and evaluation of embedded models that control the Marso robot.
• Make sure models stay reliable through every deployment stage: PoCs, field tests, and production rollout in general.
• Help define and assess data quality.
• Automate dataset extraction for model training.
• Cut the time-to-value between AI model development and production deployment.
• Build internal tools to visualize and debug data and model performance.

Skills

Python

Deep learning

Production deployment

Data pipelines

Autonomous testing

ROS/ROS2

Tools

PyTorch

TensorFlow

Job description

MARSO Robotics is seeking an AI Engineer to own the full lifecycle of embedded AI models for the Marso robot, from data quality and training to production reliability and tooling that speeds up development-to-deployment.

You will contribute to developing, training, and evaluating models, ensure reliability across PoCs, field tests and production, and build data pipelines and internal tools to monitor model performance.