Senior Software Engineer
2 days ago
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 (e.g., S3), 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.
- 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 (e.g., S3).
- Experience with cloud platforms (e.g., AWS, GCP, Azure).
- Solid understanding of database systems (SQL/NoSQL).
- 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.
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