Software Engineer - AI Data & Simulation

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 SW Engineering team. This role is central to our AI-first strategy. You will build the core software architecture that bridges the gap between our robotic hardware, high-fidelity simulations, and the "AI Brain." You will be responsible for engineering the systems that allow Menteebot to reason, remember, and learn in real-time.

Responsibilities

• Core Software Development: Design and implement high-performance, scalable software solutions primarily using Python, focusing on modularity and system reliability.
• AI & Reasoning: Develop the robot's "brain" by integrating LLMs, Agentic AI, and MCP (Model Context Protocol) with robotic tools to enable complex decision-making and full autonomy.
• Data & Simulation Infrastructure: Design robust Python APIs and pipelines for robotic simulation control, large-scale ML data ingestion (ETLs), and real-time system observability.
• Robot Memory: Architect and implement database solutions that act as the robot's persistent memory, allowing for continuous learning, state tracking, and contextual adaptation.
• Collaboration: Work closely with AI researchers to translate experimental models into production-grade software and define schemas for multi-modal robotics data (video, sensor streams, teleoperation logs).

Requirements

• 5+ years of experience as a Software Engineer with a focus on Backend, Systems, or ML Infrastructure.
• Python Expertise: Extensive experience and strong proficiency in Python – a must-have.
• System Architecture: Proven ability to build robust APIs and integrate complex software components (Simulation, AI models, Databases).
• Data Pipeline Experience: Solid understanding of building efficient ETLs and data flow architectures.
• Database Knowledge: Experience with SQL/NoSQL systems for implementing state management or memory.
• Bonus: Experience with LLM orchestration (LangChain, CrewAI), robotic frameworks (ROS2, Isaac Sim), or cloud platforms (AWS/GCP).

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.