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About the Role

Develop geospatial and remote-sensing methods that turn imagery and spatial data into reliable analytical products. You'll work in Python on change detection, object detection, segmentation, and spatial and temporal analysis. Your methods will span classical machine learning, statistical and image analysis, and deep learning architectures such as Vision Transformers (ViTs) and U-Nets, taking the approaches that prove useful from exploration through evaluation into repeatable platform capabilities. The work includes open satellite time series and commercial optical, thermal, and SAR imagery. You'll help choose appropriate data and methods, establish what the results can support, and work with engineers to make them available to analysts inside Sentinel.

Responsibilities

Develop change-detection workflows, including Sentinel-2 time series, and distinguish meaningful change from seasonality, cloud and shadow effects, acquisition differences, and registration errors.
Build and evaluate object-detection, segmentation, and classification methods for satellite imagery, using statistical techniques, classical image processing, and machine learning where appropriate.
Evaluate state-of-the-art deep learning approaches pragmatically against simpler baselines, weighing accuracy, label requirements, generalization, inference cost, and real production viability.
Assess the suitability of different sensors, resolutions, acquisition conditions, and processing levels for each use case.
Extend methods across optical, multispectral, thermal, and SAR data as requirements develop.
Design preprocessing and feature extraction with data engineers: quality masking, compositing, co-registration, normalization, spectral indices, and sensor-specific corrections.
Combine raster outputs with vector and temporal data for spatial statistics, zonal analysis, anomaly detection, and comparison across areas and observation periods.
Build or source reference datasets and evaluation protocols.
Use spatially and temporally separated validation, measure false positives and missed detections, and examine performance across regions and sensors.
Deliver traceable results with source references, timestamps, confidence or uncertainty measures, and documented limitations.
Help analysts understand when a result needs closer review.
Package tested Python methods for repeatable batch processing or inference.
Work with data and platform engineers on runtime, memory, monitoring, and integration into Sentinel workflows.

Requirements

Formal university training in geoinformatics, remote sensing, Earth observation, applied mathematics, computer science, or a related discipline, combined with substantial hands-on geospatial or remote-sensing experience.
Strong Python and scientific computing skills, using tools such as NumPy, pandas, SciPy, xarray, GeoPandas, and Rasterio/GDAL.
Practical machine-learning experience with scikit-learn and PyTorch or an equivalent framework, plus TorchGeo or related geospatial deep learning packages, including training, evaluation, and adapting existing models.
A solid understanding of remote-sensing fundamentals: spatial, spectral, radiometric, and temporal resolution; coordinate systems; image alignment; and data-quality limitations.
Experience with satellite image analysis and at least one relevant task such as change detection, segmentation, object detection, or land-cover classification.
Sound statistical judgment around sampling, spatial autocorrelation, data leakage, class imbalance, uncertainty, and generalization to new places and acquisition conditions.
An engineering-minded approach to research: versioned code and data, reproducible experiments, tests, and clear explanations of how a method performs and fails.
Clear communication in English with both technical colleagues and domain specialists.
Eligible to work in Germany.

Skills

Python
Change detection
Object detection
Segmentation
Spatial analysis
Temporal analysis
Classical machine learning
Statistical analysis
Image analysis
Deep learning
Vision Transformers (ViTs)
U-Nets
NumPy
pandas
SciPy
xarray
GeoPandas
Rasterio/GDAL
scikit-learn
PyTorch
TorchGeo
Remote sensing fundamentals
Satellite image analysis
Land-cover classification
Statistical judgment
Reproducible experiments
Versioned code and data
Thermal infrared imagery
SAR data analysis
Geospatial foundation models
Dask
Apache Spark/Sedona
Zarr
GPU inference optimization
PyTorch/CUDA
ONNX Runtime with TensorRT
NVIDIA Triton Inference Server
Spatial SQL
DuckDB
PostGIS
STAC-based data discovery
QGIS
geemap
COG
GeoParquet
Commercial imagery analysis
Agentic software development

Location

Germany
Remote

Work Type

Remote-first
Full-time

Experience Level

Substantial hands-on geospatial or remote-sensing experience
PhD (preferred)

Education Level

Formal university training in geoinformatics, remote sensing, Earth observation, applied mathematics, computer science, or a related discipline
PhD in geoinformatics, remote sensing, Earth observation, or a related field (preferred)

Benefits

30 days of vacation
Equipment support
Learning support

About the Company

Orcrist builds secure data intelligence software for defense, law enforcement, and enterprise teams.
Our Sentinel platform combines data integration, AI-assisted analysis, and operational workflows.
The GEOINT team is extending it with geospatial data services, remote-sensing capabilities, and a web-based common operational picture.