Edge Platform Engineer

Movendi Inc.; San Francisco (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Movendi Inc. is building a secure software platform that deploys, operates, and manages Movendi applications across thousands of industrial edge devices, robots, and gateways.

You will craft the runtime environment enabling Reality Models, Vitesse services, digital twins, and industrial apps to run reliably at the edge, even when disconnected or resource-constrained. You will design and develop the Movendi Edge Platform, build the runtime for AI services and industrial applications, and manage

Qualifications

• Strong software engineering experience in C++, Go, Rust, or Python.
• Deep understanding of Linux operating systems and system administration.
• Experience with Docker and containerized applications.
• Strong networking fundamentals (TCP/IP, VPNs, TLS, DNS, routing, firewalls).
• Experience with Infrastructure as Code and deployment automation.
• Familiarity with Kubernetes or lightweight edge orchestration (K3s/MicroK8s).

Responsibilities

• Design and develop the Movendi Edge Platform.
• Build the runtime environment for AI services, digital twins, and industrial applications.
• Manage application lifecycle, service discovery, and dependency management.
• Optimize startup times, resource utilization, and system reliability.
• Accelerate fleet management, including secure onboarding and remote updates.

Skills

C++

Go

Rust

Python

Linux

Networking

IaC

Docker

Kubernetes

Tools

Docker

Kubernetes

OpenTelemetry

Job description

Develop the software platform that securely deploys, operates, and manages Movendi applications across thousands of industrial edge devices, robots, and intelligent gateways. You will build the runtime environment that lets Reality Models, Vitesse services, digital twins, and industrial applications execute reliably at the edge, even in disconnected, resource-constrained, and mission-critical environments.

• Design and develop the Movendi Edge Platform.
• Build the runtime environment for AI services, digital twins, and industrial applications.
• Manage application lifecycle, service discovery, and dependency management.
• Optimize startup times, resource utilization, and system reliability.

Fleet management

• Design secure device onboarding and provisioning.
• Develop centralized fleet management capabilities.
• Build remote software deployment and rollback mechanisms.
• Implement over-the-air software and firmware updates.
• Monitor health, configuration, and software versions across deployed systems.
• Enable large-scale management of heterogeneous hardware platforms.

Connectivity and reliability

• Design resilient communication between edge devices and cloud services.
• Support intermittent or disconnected operation with automatic synchronization.
• Develop secure messaging and telemetry pipelines.
• Build self-healing capabilities and automated recovery after failures.
• Improve platform observability and operational diagnostics.

Security and hardware

• Secure device identity and lifecycle management.
• Implement secure boot, trusted execution, and encrypted storage where applicable.
• Protect software distribution pipelines against supply-chain attacks.
• Support industrial PCs, gateways, embedded Linux systems, and robotic controllers.
• Optimize deployments for ARM and x86 platforms.

Required

• Strong software engineering experience in C++, Go, Rust, or Python.
• Deep understanding of Linux operating systems and system administration.
• Experience with Docker and containerized applications.
• Strong networking fundamentals (TCP/IP, VPNs, TLS, DNS, routing, firewalls).
• Experience with Infrastructure as Code and deployment automation.
• Familiarity with Kubernetes or lightweight edge orchestration such as K3s or MicroK8s.

Preferred

• Embedded Linux platforms and ARM-based systems.
• Industrial gateways or IoT platforms.
• MQTT, OPC UA, Modbus TCP, EtherNet/IP, or other industrial protocols.
• Secure OTA update systems, TPMs, secure boot, and hardware root of trust.
• Observability platforms such as Prometheus, Grafana, OpenTelemetry, or ELK.
• Cloud platforms and hybrid edge-cloud architectures.
• Deploying AI inference at the edge, including GPU or NPU acceleration.
• Fleets of hundreds or thousands of remotely managed devices.