Edge Vision and Embedded Systems

Nuvnext; Región Centro; Hybrid; MXN 600,000 - 800,000; Full time

Job summary

Nuvnext in Guadalajara is seeking an edge AI engineer who can optimize detection and tracking models for local hardware. You will work in person, on-site and in the field, selecting hardware for each site and ensuring the solution runs without internet.

You will measure real performance on site, validate memory usage, and push real-time improvements within the first 90 days. The role combines firmware-ish work with software tuning, and you will collaborate directly with founders to ship robust

Qualifications

• Python and C/C++ experience is required.
• Experience with hardware: boards, cameras or sensors.
• Ability to measure performance and memory usage.
• Willingness to travel and fix things on site.

Responsibilities

• Optimize detection and tracking models for edge hardware.
• Choose and test hardware for each site type.
• Make equipment work offline and recover autonomously.
• Measure real performance on site, not in the lab.

Skills

Python

C/C++

Tools

Embedded Linux

RTSP

ONVIF

Job description

Engineering Guadalajara · in person and in the field Full time

The client's video never leaves their site. You make vision models run on the hardware that is there: with little memory, no big GPU and no reliable internet.

The role

• Optimize detection and tracking models for edge hardware.
• Choose and test hardware for each type of site.
• Make the equipment work without internet and recover on its own.
• Measure real performance on site, not in the lab.

Your first 90 days

• Day 30

  You run the vision pipeline on an edge device and measure it.

• Day 60

  You deliver an optimized version that runs in real time.

• Day 90

  A client site runs on your configuration.

Must have

• Python and some C or C++.
• You have worked with hardware: boards, cameras or sensors.
• You know how to measure performance and memory.
• Going on site to fix things doesn't scare you.

Nice to have

• Model quantization and optimization.
• Embedded Linux.
• RTSP and ONVIF.

What working here is like

What you build goes into products that operate real organizations. You work directly with the founders, with no layers between your code and production, under the same rule we give the intelligence: a person signs what is critical.