Founding Edge AI Engineer — Embedded Systems & Time-Series

Inframesh; Greater London; Hybrid; GBP 75,000 - 110,000; Full time

Job summary

Inframesh is building the intelligence layer for buildings. We seek an engineer to own the software path from raw electrical signals to the product interface, working across embedded Linux, data acquisition, edge inference, APIs and the first production dashboard.

This hands-on role spans hardware integration, system-level debugging and delivering end-to-end software for a real-time energy monitoring product. London hybrid work is expected with frequent hardware collaboration.

Qualifications

• Strong Python and Linux experience.
• Experience deploying software onto devices outside your development environment.
• Experience with time-series, sensor or signal-processing data.
• Practical experience deploying or adapting ML models and inference pipelines.
• Ability to build/maintain a React or Next.js interface from designs.
• Good ownership mentality across cross-functional boundaries.

Responsibilities

• Build and harden the software running on our embedded Linux devices.
• Own the data path from sampling through buffering, timestamping, processing and labelled records.
• Integrate and optimise our ML inference pipeline on target hardware.
• Tune inference for latency, memory usage, stability and long unattended operation.
• Build APIs and event streams for the dashboard and external partners.
• Own device provisioning, connectivity recovery, logging, remote diagnostics and OTA updates.
• Take Next.js/React app and Figma designs to production deployment.
• Build live product views for power data, appliance activity, consumption history and events.
• Collaborate with firmware and hardware engineers during integration and field deployment.

Skills

Python

Linux

Time-series data

ML model deployment

React/Next.js

Hardware debugging

Ownership mindset

Job description

Inframesh is building the intelligence layer for buildings. We seek an engineer to own the software path from raw electrical signals to the product interface, working across embedded Linux, data acquisition, edge inference, APIs and the first production dashboard.

This hands-on role spans hardware integration, system-level debugging and delivering end-to-end software for a real-time energy monitoring product. London hybrid work is expected with frequent hardware collaboration.