About the role

Muon Space is building and operating a growing portfolio of infrared (IR) space missions that generate mission-critical Earth observation data for a diverse and expanding set of customers. These missions include the FireSat constellation, which delivers calibrated IR imagery and derived wildfire products to wildfire operators, the scientific community, the US Government, and commercial partners; and the Space Based Environmental Monitoring (SBEM) mission, which provides data for weather and cloud characterization, situational awareness, and a broad range of additional use cases. Muon's IR Data Products team owns the multi-mission, multi-customer software systems that process, store, and disseminate the data from these missions - turning raw downlink into calibrated imagery and derived products, and delivering those products reliably to customers at scale.

In this role, you will build, harden, and operate the production pipelines and services that process and serve data from Muon's Infrared missions - with an emphasis on robust processing pipelines, low latency, strong observability, and geospatial data serving. The ideal candidate is a hands-on senior engineer who has shipped and operated production geospatial or scientific data pipelines at scale. They combine strong software engineering fundamentals with real geospatial domain knowledge and take pride in building systems that are legible, observable, and easy for the rest of the team to reason about.

In this role, the candidate will initially focus on three key areas:

• Robust production pipelines: Design, implement, and operate high-reliability data pipelines that process captures from L0 through L1 imagery, multispectral stacks, orthomosaic tiles, and derived wildfire products. Implement robust error handling, idempotency, caching, reprocessing, and safe rollout across the trunk-based release process.
• Low-latency optimization: Design and implement improvements to the software system that minimize the end-to-end latency of data processing and dissemination. Optimize cloud infrastructure and services, implement algorithm speed-ups, and reduce computational cost.
• Geospatial data hosting and serving: Build and operate data hosting and serving services that support internal and customer-facing search and retrieval, visualization, and data dissemination. Develop and implement schemas, ingest paths, a STAC catalog, and query APIs.

Additionally, the role will partner closely with our Remote Sensing Data Scientists to harden research code into production stages, and will interface with other software teams such as Data Platform and Flight Software on shared contracts (capture metadata, navigation products and telemetry interfaces).

This position is hybrid and requires working on-site in our Denver, CO office three days per week.
Responsibilities

• Design, build, and operate the production pipelines that process IR Mission captures through L1 imagery, multispectral stacking, geolocation, orthomosaic tiles, hotspots, and derived wildfire products.
• Optimize pipeline infrastructure, services, and algorithms to minimize latency.
• Design and build geospatial data discovery and data-serving infrastructure and APIs used by internal product developers and downstream customers.
• Design and build internal data-visualization and inspection tools for geospatial data.
• Maintain and improve the software testing and release infrastructure and processes.
• Partner with Remote Sensing Data Scientists to productionize research code - hardening error paths, adding tests, adding observability, and making it re-runnable and idempotent.
• Build observability of the IR data pipeline end-to-end: structured logging, dashboards, metrics and alerts.

Qualifications

• Bachelor's degree with 8+ years or MS with 6+ years or PhD with 3+ years of relevant software engineering experience.
• Demonstrated experience shipping and operating production Python data pipelines and backend services on AWS or comparable cloud infrastructure.
• Hands-on experience with a modern workflow orchestrator (e.g., Flyte, Airflow, Prefect, Argo, Dagster, or similar) and containerized deployments.
• Strong software engineering fundamentals: production Python code, unit and integration testing, Git-based trunk development, code review, CI/CD, etc.
• Demonstrated experience adding production-grade observability to a system: structured logging, metrics, dashboards, alerts, and on-call ownership.
• Working geospatial domain knowledge - hands-on experience with common geospatial libraries (e.g., GDAL/OGR, rasterio, PROJ, shapely) and formats (e.g., GeoTIFF, Cloud Optimized GeoTIFF, NetCDF, Zarr, HDF).
• Ability to work with a distributed, interdisciplinary team of scientists, engineers, and program management.

Preferred Qualifications

• Experience building and operating a STAC catalog (stac-fastapi, pystac, pgstac, or equivalent) and associated ingest and query paths.
• Experience with xarray / Zarr / NetCDF at scale, including chunked processing and streaming reads from S3.
• Experience building internal data-visualization or inspection tools (dashboards, map viewers, notebook-based workflows) that support scientific debugging.
• Experience with Grafana, LogQL, Prometheus, or InfluxDB in production.
• Experience with satellite ground-segment concepts (downlink, telemetry, capture / swath structure) or other remote-sensing data pipelines.
• Experience with Flyte / Union.ai specifically.
• Terraform / IaC experience for AWS resources.
• Experience productionizing research or scientific code written by non-engineers.

Salary

The salary range for this role is $192,000 - $213,000, plus a competitive equity grant and comprehensive benefits package. Final compensation will be determined based on skills, qualifications, experience, and geographic location as assessed during the interview process.