AI role · Open offer

Senior Backend Engineer — Geo data pipelines

Focus: Geospatial AI · Earth observation · Location intelligence

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

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.

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At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: 10 Rue de Turbigo, Paris, IDF 75001, France
Compensation: EUR 82k-104k/year + remote stipend
Experience level: Senior
Team: Platform & data
Benefits & perks

About this role

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

Geoox is hiring a Senior Senior Backend Engineer — Geo data pipelines on the Platform & data 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 — Own ingestion and serving for large geospatial datasets: tiling, spatial indexes, ETL from imagery providers, and APIs consumed by web and mobile clients.

Focus: reliability, cost-aware batching, and observability for jobs that span TB-scale assets. Collaborate closely with ML on feature stores that respect CRS and resolution metadata.

Details: 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.
PostgreSQL
Practical PostgreSQL experience applied to vision, sensor, or geospatial models in production in production or rigorous pilots.
Kubernetes

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.
PostGISTool
Regular use of PostGIS 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: Link a repo or design note showing a pipeline you hardened for geo or time-series workloads.
Several of these show up in recent shipped work—not only on your CV: Python, PostgreSQL, Kubernetes, AWS, 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 · 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.

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What the employer asked for

Link a repo or design note showing a pipeline you hardened for geo or time-series workloads.

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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