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.

Todas las ofertas

Rol IA · Oferta abierta

Staff ML Engineer — Geospatial & Earth observation

Enfoque: Geospatial AI · Earth observation · Location intelligence

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

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

Este anuncio está pensado para talento nativo en IA: habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.

4 habilidades2 herramientas

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Empleo: Tiempo completo
Modalidad: Hybrid
Ubicación: 52 Boulevard de Sebastopol, Paris, IDF 75003, France
Compensación: EUR 115k-145k/year + equity
Nivel de experiencia: Staff
Equipo: Geospatial ML
Beneficios

Sobre el rol

Qué busca el equipo y cómo encaja el puesto.

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.

Habilidades que buscamos

Qué implica cada capacidad en este rol — no solo una lista de palabras clave.

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

Herramientas y stack

Plataformas y productos mencionados para este rol (APIs, IDEs, BBDD vectoriales, CRM, diseño, 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.

Encajas muy bien si…

Adaptado a esta oferta — úsalo para comprobar tu encaje antes de aplicar.

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.

Preguntas frecuentes

Respuestas rápidas para candidatos y equipos que revisan esta oferta.

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.

Aplicar

Solo cuentas candidato: añade tu titular y bio en , y opcionalmente un enlace o PDF de CV para que los reclutadores vean tu CV completo. Luego escribe un breve pitch — las candidaturas usan el correo de tu cuenta.

Lo que pide la empresa

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

Inicia sesión con cuenta candidato para aplicar

Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.

Iniciar sesión Crear cuenta candidato

Sugerencias

Talento IA que podría interesarte

Perfiles ordenados por solape con habilidades y herramientas de este rol — útil si contratas equipo o comparas candidatos.

Aún no hay coincidencias cercanas en el directorio — prueba con tus filtros.

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Otros anuncios que comparten habilidades o herramientas — útil para stacks comparables o alternativas.

Staff ML Engineer — Geospatial & Earth observation

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Preguntas frecuentes

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?+

Talento IA que podría interesarte

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