Role Summary
We are looking for a highly motivated Field Deployment Engineer with a strong passion for learning new technologies and a drive to solve customer problems. In this role, you will be responsible for deploying quadruped robots (robot dogs), the LocalMind dispatch system, robotic arms, cameras, sensors, and Edge AI systems into real-world customer environments.
This role does not require you to be a robotics or AI expert from day one. What we value more is your ability to learn new technologies quickly, your willingness to confront real-world issues on-site, and your capability to clarify, track, and resolve problems across customers, PMs, R&D, and hardware teams.
You will participate in POCs, Demos, Pilots, official deployments, and user acceptance testing (UAT). Additionally, you will assist the Product Management department in transforming field issues into product requirements, bug reports, roadmap inputs, and deployment SOPs. You will serve as the technical eyes of the Product Management team on-site and play a critical role in scaling our AI Robotics from a simple demo to a deployable, acceptable, maintainable, and replicable product.
Key Responsibilities
- Field Deployment: Responsible for the on-site deployment of quadruped robots, LocalMind, cameras, sensors, Edge AI devices, robotic arms, and integration with customer systems.
- Site Assessment & Testing: Execute site surveys, network verification, equipment installation, system configuration, POC testing, and UAT acceptance.
- Robot Configuration: Assist in establishing robot dog inspection routes, maps, task scheduling, no-go zones, slowdown zones, and recharging strategies.
- Environment Setup: Deploy LocalMind on-premise/edge environments, integrating robots, cameras, sensors, AI models, and workflow management.
- Troubleshooting: Diagnose and troubleshoot on-site issues, including network, hardware, software, AI models, sensors, environmental constraints, and operational workflows.
- Issue Documentation: Collect logs, videos, screenshots, version information, and reproduction steps, translating them into actionable issues for PMs and R&D teams.
- Cross-functional Collaboration: Partner with PMs to evaluate customer requirements, product limitations, acceptance criteria, risks, and priorities; track issues with AI Software, Robotics R&D, Hardware, and QA teams until closure.
- Technical Documentation: Establish deployment SOPs, checklists, troubleshooting guides, UAT reports, and customer training documentation.
Requirements
- Problem-Solving Drive: Passion for breaking down problems; proactive in learning, researching, and testing when encountering unfamiliar technologies.
- Relevant Experience: Proven experience in system integration, FAE, IT/OT, AIoT, automation equipment, industrial PCs (IPC), network engineering, software/hardware testing, or technical customer support.
- Technical Foundations: Familiarity with basic Linux operations, network configuration, log analysis, and system troubleshooting.
- Analytical Skills: Ability to organize chaotic on-site issues into clear issue tracking tickets, documentation, and next action items.
- Industry Interest: High level of interest in AI, robotics, Edge AI, VLM/LLM, Local AI Runtime, or industrial automation.
- Communication & Resilience: Excellent communication skills and high stress tolerance; willing to travel and provide on-site support.
- Integrity: Honest in reporting issues without sugarcoating or hiding problems; dedicated to tracking issues until fully resolved.
Preferred Qualifications
- Robotics & Automation: Experience in implementing or deploying robots, robotic arms, AMR, AGV, drones, quadruped robots, or factory automation systems.
- Technical Ecosystems: Familiarity with Jetson, Docker, ROS / ROS2, SLAM, LiDAR, Camera, RTSP, MQTT, Modbus, or OPC UA.
- Scripting Skills: Proficiency in Python or Bash scripting.
- Domain Knowledge: Field project experience in factories, energy sectors, security, warehousing, retail, or public domains.
- Advanced AI Knowledge: Experience with AI models, VLM/LLM, RAG, Agents, Edge AI, or on-premise deployment.