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Salary: USD 212,000 - 233,000 / annual
Muon Space, a LEO satellite constellation provider founded in 2021, is expanding its High Performance Compute (HPC) team to accelerate mission-critical data products. This role works directly with the team producing image data products for ground and flight systems.
You will design and implement image processing algorithms and machine learning models that power Muon's ground and flight products, including segmentation, feature classification, labeling, and traditional image computation. The work emphasizes low latency and deployability onto cloud data centers and edge systems. You will own the processing and ML data engineering foundation, working across the full stack from algorithm design through production deployment.
Key responsibilities include:
- Design, train, evaluate, and iterate on image processing models for ground processing and onboard flight systems
- Build and own the image and ML data engineering foundation
- Optimize algorithms and models for low-latency operation on cloud compute and edge/onboard deployment in constrained environments
- Contribute to high-performance reference algorithms and implementations, benchmarking them for performance
- Hand off algorithms and models into the cloud serving stack, partnering with mission and payload teams to define requirements and acceptance criteria
- Own verification and validation, including unit, integration, and performance regression tests
This is a hybrid position requiring three days per week on-site in the San Jose, CA office.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or related technical field
- 3+ years of professional experience developing, shipping, and operating image computation or ML systems
- Strong PyTorch skills, including designing, training, and evaluating models end-to-end
- Demonstrated production experience with low-latency image computation models deployed as containerized workloads on cloud infrastructure (AWS preferred)
- Hands-on ML data engineering: dataset curation, labeling, and versioning for training and evaluation; measuring and improving algorithmic quality and performance
- Strong Python skills beyond notebooks: tested, packaged, code-reviewed code that runs unattended in production
- Strong written and verbal communication and ability to lead and conclude cross-functional technical discussions
- Ability and willingness to obtain and maintain a U.S. security clearance (active clearance is a plus)
Nice-to-have skills: geospatial or remote-sensing imagery (optical, IR, SAR); active-learning, data-centric ML, or labeling workflows; deploying computer vision or image processing models to edge or embedded targets; GPU acceleration (CUDA, HIP, or similar); CI/CD for containerized workloads including performance regression testing; direct exposure to space, aerospace, or mission-critical software environments.
