AI Data Engineer - Robotics & Multimodal System

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 Senior Software Engineer to join our AI Platform team. This role is central to our "data-focused" strategy. You will build the core data infrastructure that fuels our AI-first approach, responsible for the entire lifecycle of our robotics data.

Responsibilities

• Design and implement high-performance, scalable software solutions, primarily using Python.
• Design and build robust, scalable ETL pipelines to ingest and transform multi-modal robotics data, including video, sensor streams (joint states), teleoperation logs, and open-source datasets.
• Architect and maintain our core data lake infrastructure, creating a "single source of truth" for tagged and versioned robotics data.
• Work closely with AI researchers to define labeling schemas, ensure data quality,
• Champion best practices in data engineering and software development within a cutting-edge robotics environment.

Requirements

• 5+ years of experience as a Software Engineer, Data Engineer, or ML Infrastructure Engineer.
• Extensive experience and strong proficiency in Python – a must-have.
• Deep understanding and hands-on experience with ETL and data pipeline development – a must-have.
• Proven experience working with data lake technologies.
• Experience with cloud platforms (e.g., AWS, GCP, Azure).
• Solid understanding of database systems (SQL/NoSQL).

Advantages

• Familiarity with robotics data (e.g., ROS bags, sensor time-series) or multi-modal data (video, text, sensor fusion).
• Experience with data annotation/labeling platforms (e.g., Label Studio, V7, or custom-built tools).
• A strong understanding of the data-centric challenges in modern AI (e.g., active learning, data curation for foundation models).
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with stream processing technologies (e.g., Kafka).
• Deep understanding of Linux.