AI role · Open offer

ML Engineer — On-device & Edge AI

Market index

Focus: Vision AI

Paris · 100-300 employees · Scale-up · GenAI: No

Full-time
Senior
Mobile intelligence
Hybrid
Stockholm, Sweden · Hybrid
650k–850k SEK

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.

3 skills1 tool

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At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: Stockholm, Sweden · Hybrid
Compensation: 650k–850k SEK
Experience level: Senior
Team: Mobile intelligence
Benefits & perks

About this role

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

Deepomatic is hiring a Senior ML Engineer — On-device & Edge AI on the Mobile intelligence team (Hybrid · Stockholm, Sweden). This is a hands-on role in computer vision & geospatial AI—not a generic “AI enthusiast” post.

You will own vision, sensor, or geospatial models in production 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.

Stockholm hardware-software team ships perception models to Nordic mobility and robotics OEMs. Battery, thermal, and safety constraints dominate every design review.

Hybrid; Swedish work authorisation or EU mobility required.

What you will do

Quantise, prune, and validate models for ARM SoC and MCU targets.
Collaborate with embedded engineers on sensor drivers and sync issues.
Run field trials with structured logging and privacy minimisation.
Document certification evidence packs (UNECE-relevant where applicable).
Explore neural architecture search within strict latency envelopes.

What we look for

4+ years shipping edge ML in automotive, drones, or industrial robotics.
PyTorch + ONNX/TFLite/CoreML comfort.
Meticulous testing discipline.

Nice to have

ROS2 integration experience.
Radar/lidar fusion exposure.

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.
TensorFlow

Tools & stack

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

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

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: Footprint (MB + mJ) you improved on a production model.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, TensorFlow, and Docker.
You have owned vision, sensor, or geospatial models in production in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Stockholm, Sweden)—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.
You read the full listing (comp: 650k–850k SEK, benefits in At a glance) and your expectations align.

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

Footprint (MB + mJ) you improved on a production model.

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.

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