Which skills are required for this Geoox role?

Strong PyTorch, Python geospatial stack (GDAL, rasterio, GeoPandas), and experience shipping models on satellite or aerial imagery. Production monitoring, reproducible experiments, and clear communication with product and GIS stakeholders are essential.

What is the compensation band for this position?

The listing advertises roughly EUR 115k–145k total compensation depending on experience and hybrid arrangement. Confirm equity, bonus, and on-site days with the hiring team when you apply.

All open roles

AI role · Open offer

Staff ML Engineer — Geospatial & Earth observation

Focus: Geospatial AI · Earth observation · Location intelligence

Geospatial intelligence & AI — satellite, mapping, and location-aware products

Full-time
Staff
Geospatial ML
Hybrid
52 Boulevard de Sebastopol, Paris, IDF 75003, France
Paris, France
EUR 115k-145k/year + equity

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: Full-time
Workplace: Hybrid
Location: 52 Boulevard de Sebastopol, Paris, IDF 75003, France
Compensation: EUR 115k-145k/year + equity
Experience level: Staff
Team: Geospatial ML
Benefits & perks

About this role

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

Geoox is hiring a Staff Staff ML Engineer — Geospatial & Earth observation on the Geospatial ML team (Hybrid · Paris, France). 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.

Geoox — Lead end-to-end models for raster and vector pipelines: cloud masking, change detection, and fusion with cadastral and mobility layers. Partner with product on evaluation harnesses that respect geographic leakage and seasonal drift.

Stack: Python, PyTorch or TensorFlow, GDAL/GeoTIFF workflows, cloud batch + online inference patterns. Experience with STAC, COG, or satellite constellations is a strong plus.

Apply on Ganloss so your profile, optional CV, and cover note reach the hiring team together. Learn more at https://www.geoox.com.

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.
AWS

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.
GDALTool
Regular use of GDAL in the computer vision & geospatial AI 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: Include one example where you shipped a geo model to production (latency, coverage, or QA metrics).
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, AWS, SQL, and Docker.
You have led computer vision & geospatial AI initiatives across teams—setting technical direction, review standards, and rollout plans others could follow.
You are comfortable with the hybrid rhythm (Hybrid · Paris, 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.

Frequently asked questions

Quick answers for candidates and hiring teams reviewing this listing.

You own production ML for earth-observation or risk use cases: model training, GDAL/raster pipelines, deployment with MLOps guardrails, and mentoring on geospatial data quality. Expect hybrid work in Paris with cross-team research handoff.

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

Include one example where you shipped a geo model to production (latency, coverage, or QA metrics).

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.

Staff ML Engineer — Geospatial & Earth observation

Frequently asked questions

AI talent you may also like

Similar open roles

ML Engineer — payments & merchant AI

AI Developer — enterprise RAG

Senior ML Engineer — payments fraud

AI Developer — customer intelligence

Senior AI Developer — telecom copilots

Staff ML Engineer — Geospatial & Earth observation

Frequently asked questions

What does a Staff ML Engineer do at a geospatial AI company?+

Which skills are required for this Geoox role?+

What is the compensation band for this position?+

AI talent you may also like

Similar open roles

ML Engineer — payments & merchant AI

AI Developer — enterprise RAG

Senior ML Engineer — payments fraud

AI Developer — customer intelligence

Senior AI Developer — telecom copilots