Backend Engineer

About the Role

You will engineer backend services and ML platforms that transform multi-sensor satellite data into actionable geospatial intelligence.

This position operates within fully air-gapped, offline deployment environments (no internet access), requiring a unique skillset in production-grade systems without reliance on cloud infrastructure.

Key responsibilities include:

• Building and maintaining backend APIs that fuse SAR and MSI satellite data for geospatial analytics.
• Designing scalable database architectures and pipelines for large-scale raster datasets.
• Deploying self-hosted ML models locally with robust monitoring, logging, and evaluation frameworks.
• Optimizing inference latency and throughput under GPU constraints in isolated hardware environments.
• Developing analyst-facing tooling that chains steps securely within intelligence workflows.

What You Will Learn

You will gain rare experience building production ML systems under strict security and offline constraints, mastering probabilistic system reasoning, applied geospatial machine learning, and the ability to self-host complex AI models without managed cloud services.

Requirements

• 1-2 years of backend or ML engineering experience with strong fundamentals in request-response lifecycles.
• Systematic debugging skills for environments where external resources like Google are unavailable at runtime.
• Demonstrated ability to learn complex technologies independently and communicate blockers clearly.
• Comfort working within security-first, air-gapped processes and handling sensitive data carefully.

Bonus Skills

• Experience with geospatial tools (GDAL, rasterio) or satellite imagery processing.
• Servicing ML models on-prem using frameworks like Triton, ONNX Runtime, or local LLMs.
• Familiarity with containerization for isolated environments and GPU-based inference.