Computer Vision

Nuvnext; Región Centro; Híbrido; MXN 420.000 - 780.000; Jornada completa

Descripción de la vacante

Nuvnext in Guadalajara is seeking an engineer to train and tune detection, segmentation and tracking models on real video, and to run them at a client site using existing cameras and recorders.

You will turn what the model sees into time-stamped events with video clips, measure false positives/negatives with the client, and determine adjustable thresholds that can be signed off. This role involves collaborating directly with founders and delivering field-ready deployments.

Formación

• You have trained or tuned a vision model and you know why it fails.
• Fluent Python and the judgment to measure, not just run examples.
• You understand a camera's limits: light, angle, resolution, compression.
• You write code that runs the same on your machine as on a client's hardware.

Responsabilidades

• Train and tune detection, segmentation and tracking models on real video.
• Make them run at the client's site, on cameras and recorders that already exist.
• Turn what the model sees into events with time, place and a video clip.
• Measure false positives and negatives with the client and decide which threshold can be signed off.

Conocimientos

Vision model training

Python programming

Model evaluation

Edge deployment

Herramientas

YOLO

OpenCV

ffmpeg

ONVIF

RTSP

Descripción del empleo

Engineering Guadalajara · in person Full time

You turn the cameras a client already has from video into decisions: what happened, where, at what time, with the clip that proves it. The video stays at the client's site.

The role

• Train and tune detection, segmentation and tracking models on real video.
• Make them run at the client's site, on cameras and recorders that already exist.
• Turn what the model sees into events with time, place and a video clip.
• Measure false positives and negatives with the client and decide which threshold can be signed off.

Your first 90 days

• Day 30

  You run the current vision pipeline on test video and deliver a measured improvement.

• Day 60

  You own a complete case: from the camera feed to the alert the shift lead receives.

• Day 90

  A model of yours runs on a client's cameras with its error rate documented.

Stack and tools

Python PyTorch YOLO OpenCV ffmpeg RTSP / ONVIF

What we look for

Must have

• You have trained or tuned a vision model and you know why it fails.
• Fluent Python and the judgment to measure, not just run examples.
• You understand a camera's limits: light, angle, resolution, compression.
• You write code that runs the same on your machine as on a client's hardware.

Nice to have

• Experience with YOLO, ByteTrack or other trackers.
• Video over RTSP or ONVIF, DVR and NVR recorders.
• Optimizing models for hardware without a big GPU.

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.