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Hydrosat Tech Stack
Thermal satellite intelligence for agricultural water management
Hydrosat applies thermal infrared satellite data and machine learning to optimize water use in agriculture. The stack—PyTorch, TensorFlow, AWS/GCP/Azure, Airflow, Kubernetes—reflects a data-intensive, cloud-native operation built for processing high-volume geospatial imagery. The engineering-heavy hiring (9 roles) alongside dedicated data (6) and research (3) teams indicates active scaling of model deployment and pipeline infrastructure, especially given repeated pain points around real-time analytics pipelines, ML model production readiness, and satellite automation.
Tech Stack 23 technologies
Core StackPyTorch TensorFlow scikit-learn AWS Docker Kubernetes Python Apache Airflow Terraform Node.js TypeScript React Grafana Datadog CloudWatch Prometheus GitLab CI/CD GitHub Actions Jenkins GCP Azure GDAL Rasterio
What Hydrosat Is Building
◆Challenges
- Scaling data pipelines for real-time analytics
- Automating satellite tasking
- Capturing high spatio-temporal resolution thermal infrared data
- Automation and observability across environments
- Ensuring reliability and security
- Scaling large-scale data pipelines
- Ensuring accurate financial reporting
- Scaling finance operations
- Deploying ml models in production
- Maintaining performance of existing pipelines
▲Active Projects
- Cloud infrastructure for large-scale data pipelines
- Scalable ml models for image classification, segmentation, and data fusion
- Evapotranspiration modeling
- Satellite tasking automation
- Deployment of ml models in production
- 2026 in-country field campaigns
- Full stack services for satellite operations
- Crop water stress modeling
- Surface energy balance modeling
- Data fusion methods
Hiring Activity
Steady25 roles · 8 in 30d
Department
Seniority
Company intelligence
