Muon Space
Software Engineer, IR Data Products
About this role
Muon Space seeks a Software Engineer to build and operate production data pipelines that transform raw infrared imagery from satellites into analysis-ready wildfire detection products under strict latency constraints. You'll work on a system serving real users and emergency responders, collaborating with remote sensing scientists to turn algorithms into reliable, scalable production code.
What you'll do
- Design and maintain production data pipelines for infrared imagery calibration, geolocation, and orthorectification
- Optimize pipeline performance to meet hard latency targets through profiling and parallelization
- Implement and operate workflow orchestration for continuous processing across multiple satellites
- Build monitoring, alerting, and operational tooling; participate in on-call rotation
- Translate remote sensing algorithms and prototypes into production-ready, tested code
- Diagnose and resolve data quality and processing failures across the pipeline
What they're looking for
- Python (production-grade)
- AWS cloud infrastructure
- Container deployment and orchestration
- Data pipeline optimization and performance profiling
- Geospatial data formats (GeoTIFF, NetCDF, COG, Zarr)
- Workflow orchestration (Flyte, Airflow, or similar)
- Remote sensing and satellite imagery concepts
- Infrastructure-as-code (Terraform)
Benefits
- Equity compensation
- Medical, dental, and vision insurance
- 401(k) retirement plan
- Short and long-term disability and life insurance
- Three weeks paid vacation for new employees
- Hybrid work (3 days/week on-site in San Jose, CA)
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Muon Space
Muon Space builds satellites and infrared remote sensing instruments for Earth observation and geospatial data collection. The company is hiring systems engineers, flight software engineers, optical engineers, and software engineers to develop spacecraft, embedded satellite systems, and internal operational software.
- Website
- muonspace.com
Likely interview questions
- Describe your experience optimizing data-intensive pipelines—what bottlenecks did you identify and how did you resolve them?
- How have you approached learning scientific domains or mathematical concepts outside your core expertise?