Mentee Robotics, now part of Mobileye, brings world-class robotics expertise, together with Mobileye's decades of leadership in computer vision, machine learning, and autonomous driving technologies. Building on that foundation, Mentee by Mobileye is creating the "MenteeBot", the AI-first humanoid robot that combines perception, reasoning, learning, and advanced dexterous manipulation, designed to work alongside people in their everyday lives, at the workplace and in the home.
At Mobileye, our technologies power more than 230 million vehicles worldwide and have helped shape the future of autonomous mobility. With teams around the world, we continue to innovate at the forefront of autonomous systems, advancing the next generation of Physical AI for real life.
What you'll do
As a Data Engineer at Mentee Robotics, you will own the data lifecycle for Physical AI and humanoid robotics: the pipelines and systems that move data from the moment it is captured to the moment it trains a model.
You will design and scale the pipelines that convert large volumes of simulation and real-robot recordings into state-of-the-art datasets — handling extraction, transformation, feature schemas, statistics, and video at scale. You will build the curation systems that generate and select high-quality training data, and the release tooling that produces versioned datasets.
You will work with various reinforcement learning and computer vision algorithms which generate realistic simulation data for humanoid robotics tasks.
You will build robust, production-grade systems that bridge data producers and consumers, accelerate dataset iteration, and make every dataset that reaches a model trustworthy, reproducible, and traceable to its source.
Requirements
B.Sc. in Computer Science, Engineering, or a related field.
4+ years of hands-on experience in data engineering, backend software engineering, or data infrastructure.
Python Expertise: Extensive experience and strong proficiency in Python — a must-have.
Data at scale: Proven experience designing and operating data pipelines and storage/serialization formats (e.g. Parquet, HDF5, Arrow) for large datasets.
System Architecture: Proven ability to build robust, well-tested data tooling and integrate complex software components into reliable pipelines.