AI role · Open offer

ML Engineer — industrial IoT

Schneider Electric

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills2 tools

Apply now Find matching talent

Share this role

At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 35 Rue Joseph Fourier, Grenoble, ARA 38000, France · Hybrid
Compensation: EUR 52k–72k/year + bonus
Experience level: Senior
Team: AI & automation
Benefits & perks

About this role

What the team is looking for and how this position fits.

Schneider Electric is hiring a Senior ML Engineer — industrial IoT on the AI & automation team (Hybrid · Grenoble, France). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Schneider Electric déploie l'IA IoT industriel depuis Grenoble.

ML Engineer : modèles prédictifs maintenance/énergie avec déploiement edge/cloud.

Missions

Feature engineering séries temporelles capteurs.
Industrialiser avec monitoring drift.

Stack & environnement

Python, PyTorch, Azure/K8s, MLOps.

Profil recherché

Expérience IoT ou industrie 4.0.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AzureTool
Regular use of Azure in the ML platform & MLOps stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Modèle prédictif maintenance ou énergie avec déploiement edge.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, MLOps, and Kubernetes.
You have owned training, deployment, observability, and cost control in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Grenoble, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Modèle prédictif maintenance ou énergie avec déploiement edge.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

Suggested

AI talent you may also like

Profiles ranked by overlap with this role's skills and tools—handy if you're hiring for a team or comparing backup candidates.

No close matches in the directory yet—try with your own filters.

Similar open roles

Other listings that share skills or tools with this one—useful if you want comparable stacks or backup options.

ML Engineer — industrial IoT

AI talent you may also like

Similar open roles

Senior ML Engineer — logistics & supply chain

ML Engineer — customer solutions

GenAI Engineer — cloud platform

Senior AI Developer — aerospace systems

Senior AI Developer — data governance copilots