The Role
Build and optimize high-throughput preprocessing and postprocessing services that transform raw satellite, aerial, and drone imagery into ML-ready inputs and production geospatial products. Implement memory-efficient, distributed worker pipelines handling diverse geospatial formats, coordinate transformations, spatial indexing, and deliverables like segmentation masks, probability maps, and GeoPackage vector outputs.
Summary Generated by Built In
Satellite and drone imagery access is on the rise, and traditional image processing methods are struggling to keep up. We’ve never had more data, and yet it’s never harder than ever to gain meaningful insights.
Our scalable AI platform enables custom model training on global features, providing real-time, on-demand geospatial insights with impressive speed and accuracy. The application turns months of manual work into mere minutes, and with much better results. We work with customers from various domains, from intelligence and defence, local and federal governments, to small and large enterprise enterprises, which requires us to have a lot of flexibility on how we deploy and maintain our services.
We kicked off in 2020 and have secured $35 million in series A funding from a lineup of top US and European investors, among which Microsoft M12, Point72 Ventures, Maxar, In-Q-Tel, SAFRAN, and ISAI/Capgemini.
We're searching for a Software Engineer to join our Data Plane team, where you'll build the preprocessing and postprocessing services that transform raw imagery into AI-ready inputs and convert model outputs into actionable geospatial products. You'll work on high-throughput data pipelines that handle terabytes of satellite, aerial, and drone imagery across diverse formats and coordinate systems.
What you'll do
- Build and optimize preprocessing pipelines that ingest, tile, and transform geospatial imagery (GeoTIFF, multispectral, SAR, COG) for downstream ML inference
- Develop postprocessing services that convert model outputs into production-ready deliverables: segmentation masks, probability maps, and vector detections in GeoPackage format
- Design resilient, memory-efficient services for processing large-scale imagery through distributed worker pools
- Work on our unified Huntr-to-Replika pipeline, automating the flow from detection outputs to 3D-ready terrain tiles with auto-generated configuration
- Tackle challenges around coordinate reference systems, spatial indexing, and data format interoperability
- Optimize for the unique characteristics of geospatial workloads: memory-bound processing, batch-heavy operations, and streaming large raster datasets
YOUR PROFILE
- Strong practical knowledge of Python with experience building data-intensive applications
- Experience with geospatial data formats, GDAL, or raster/vector processing
- Understanding of coordinate reference systems and spatial data transformations
- Experience with data pipelines, ETL processes, or batch processing systems
- Familiarity with async processing patterns, task queues (Redis), and worker architectures
- Solid understanding of PostgreSQL and working with large datasets
- Strong software engineering fundamentals: testing, CI/CD, observability, reliability
- You're outcome-oriented and enjoy optimizing systems for performance and scale
- Hands-on experience with Kubernetes in production environments
- Familiarity with imagery processing or computer vision pipelines
- Experience with memory optimization for large file processing
- Background deploying systems in regulated or air-gapped environments
Ideally:
Tech Stack
- Python, FastAPI
- PostgreSQL, Redis
- GDAL, rasterio, GeoPandas
- Docker, Kubernetes (EKS, K3S)
- AWS (with on-prem and edge deployment targets)
Why join us?
- Own the data backbone: Your services are the foundation that every detection and 3D reconstruction flows through
- Geospatial at scale: Process imagery spanning continents, handling formats from commercial satellites to tactical drones
- Diverse deployment challenges: Build systems that run in AWS, on customer infrastructure, or on a laptop in the field
- Growth trajectory: Join ahead of our Series B as we expand into new markets and scale the platform
- Strong technical culture: Work alongside ML Engineers, GIS specialists, and 3D graphics engineers solving novel problems
- Healthy work-life balance with flexible working arrangements
- Competitive compensation with personalized benefits including learning opportunities, mental wellbeing programs, and healthcare
The minimum salary is 56.000,--€ gross per year. The effective salary depends on qualification and experience and may be significantly higher!
Skills Required
- Strong practical knowledge of Python
- Experience with geospatial data formats and raster/vector processing (GeoTIFF, COG, SAR, multispectral)
- Experience with GDAL, rasterio, GeoPandas
- Understanding of coordinate reference systems and spatial data transformations
- Experience with data pipelines, ETL processes, or batch processing systems
- Familiarity with async processing patterns, task queues and worker architectures (Redis)
- Solid understanding of PostgreSQL and working with large datasets
- Strong software engineering fundamentals: testing, CI/CD, observability, reliability
- FastAPI
- Docker
- AWS (cloud deployment and on-prem/edge targets)
- Hands-on experience with Kubernetes in production environments
- Familiarity with imagery processing or computer vision pipelines
- Experience with memory optimization for large file processing
- Background deploying systems in regulated or air-gapped environments
Am I A Good Fit?
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The Company
What We Do
Blackshark.ai is a geospatial intelligence company that leverages cutting-edge AI and satellite technology to create a 3D digital twin of Earth. It transforms global satellite and aerial imagery into structured models of the physical world, enabling object detection and simulation. Its technology provides actionable insights for various industries, including urban planning, autonomous driving, and defense, empowering users with high-frequency 3D mapping applications.
