About the role
Muon Space is expanding its High Performance Compute (HPC) team to accelerate mission-critical data products and unlock a growing pipeline of image processing and ML workloads across ground and flight systems.
This role works directly with the team that produces our image data products. You'll take the most expensive stages of our imaging pipelines, including bundle adjustment, orthorectification, image I/O and reprojection, and re-implement them as high-throughput, low-latency services, with hardware acceleration where possible. This is a hands-on implementation role for a classical imaging specialist who wants to see their algorithms running in production at scale.
This position is hybrid and requires working on-site in our San Jose, CA office three days per week.
Responsibilities
- Implement and re-implement expensive imaging-pipeline stages in performant C++, including bundle adjustment, orthorectification, and image format handling, and expose them cleanly to Python.
- Drive down IR Data Product pipeline latency by replacing expensive stages with optimized implementations (e.g. GPU-based orthorectification), and extend the same approach to other imaging data product and missions.
- Work with the image data products team to identify the highest value stages to port from Python to C++/GPU and drive the migrations to completion and success.
- Contribute to high-performance reference algorithms and benchmark them.
- Partner with the HPC GPU specialists where workloads benefit from GPU acceleration.
- Own verification and validation, including unit, integration, and performance regression tests in CI.
- Produce clear designs, trade studies, and interface documentation.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related technical field.
- 3+ years of professional experience developing performance-critical C++ software in production.
- Expert-level C++, with deep familiarity with memory management, concurrency, and performance-oriented programming.
- Hands-on experience with bundle adjustment, orthorectification, and common image and geospatial formats.
- Demonstrated experience building high-throughput, low-latency image-processing services.
- Experience integrating C++ components into Python pipelines.
- Strong Python skills.
