Data Engineer

Description Mentee Robotics is redefining humanoid automation with an AI-first approach, integrating cutting-edge perception, reasoning, and dexterous manipulation into a fully autonomous humanoid robot that continuously adapts and learns. Our flagship product, Menteebot v3, is designed to seamlessly integrate into industrial, logistics, and retail environments, performing complex tasks with human-like adaptability. We are looking for an experienced Data Engineer to build the foundational data infrastructure that turns raw simulation and real-robot recordings into curated, traceable, training-ready datasets — the fuel that powers Menteebot's intelligence. Responsibilities 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.