Role Overview
Forward Deployed Engineers for the Robot Operation System work at the intersection of cloud infrastructure and robotic systems. You will design, develop, deploy, and maintain edge-connected applications that integrate robots, shelves, and vision systems with our core cloud backend platform. The role bridges software engineering and field deployment to secure reliability and scalability across the robot operation stack—spanning cloud APIs to Jetson-based devices running ROS and C++ systems—while partnering with backend engineers, robotics engineers, and field operation teams.
Team & Platform Context
We develop using C# (.NET) for both backend and edge communication services to ensure unified development across systems. Robots run ROS/C++ on NVIDIA Jetson AGX devices and connect to cloud services through gRPC and MQTT. You will work across this stack—implementing backend agents, on-device services, and deployment pipelines with Ansible and Docker—to deliver seamless cloud-to-edge operations.
Tech Stack
- Frontend: TypeScript, React, C#, Blazor, (Tailwind, shadcn)
- Backend: .NET, TypeScript
- Cloud Infra: Terraform, Azure, Kubernetes
- Data Stores: PostgreSQL, MongoDB, Redis
- IoT/Edge: .NET, C++, Python, ROS, NVIDIA Jetson AGX, IoT boards
- CI/CD: GitHub Actions, SonarQube, Ansible
Key Responsibilities
Edge Software Development
- Develop and maintain C# services and agents that connect ROS-based systems with the cloud backend.
- Implement telemetry, monitoring, and diagnostic subsystems for robots and shelves.
- Optimize communication reliability and performance for large-scale IoT deployments.
- Debug low-level .NET clients with C++ modules to ensure real-time stability and efficiency.
System Integration & Deployment
- Build and test deployment pipelines for NVIDIA Jetson AGX and similar Linux-based devices using Azure Container Registry, Ansible and Docker.
- Integrate telemetry and job execution pipelines between the cloud backend, robot control systems, and vision processing systems.
- Collaborate with robotics engineers to align ROS topics, services, and messages with .NET components.
Production Readiness & Operations
- Support deployment validation in development and production environments.
- Partner with operations teams to analyze robot logs and improve stability and observability.
- Contribute to incident response and root-cause analysis for field issues.
Collaboration & Continuous Improvement
- Collaborate with backend, robotics, and vision teams to enhance cross-system communication and reliability.
- Contribute to documentation, CI/CD pipelines, and quality improvement initiatives.
Qualifications - Must Have
- 6+ years of professional experience as a software engineer.
- 4+ years of hands-on experience with C#/.NET.
- Ability to read and write production-level C++.
- 3+ years of experience developing applications for real users, including release and operation phases.
- 2+ years working with embedded systems, edge computing, or IoT devices.
- Experience in team development using Git with 5 or more engineers.
- Experience building software for edge or IoT devices running Linux (ARM64 preferred).
- Experience with containerized deployments (Docker) and remote provisioning (Ansible).
- Knowledge of gRPC, MQTT, WebSocket, or similar communication protocols.
- Experience integrating telemetry and monitoring systems for distributed environments.
- Comfortable collaborating with hardware and field operations teams, thriving in fast-paced environments, and open to occasional travel for on-site support.
Nice to Have
- Robotics, computer vision, or automation systems experience.
- Knowledge of Azure IoT Hub, Azure Container Registry, or related cloud services.
- Familiarity with ROS/ROS2 concepts including nodes, topics, services, and launch files.
- Familiarity with web-based visualization and debugging tools.
- Background in performance optimization for real-time systems.
- Understanding of security and authentication mechanisms for device communication.
- Experience deploying robotics or large-scale IoT systems in production environments.
- Exposure to retail automation or supply chain technology.
- Experience deploying machine learning models on edge devices (TensorRT, ONNX).