Staff Software Engineer - Payload

Responsibilities

• Coordinate, code, and lead implementation of cluster management and workload coordination systems and tools to manage the HPC
• Implement and test highly optimized GPU-aware containerized image processing workloads on the cluster
• Work hand in hand with Security, Export Compliance, and DevOps/Platform engineering to ensure we can deploy and maintain test, qualification, and flight-level updates
• Operate in a lean startup environment, maintaining a laser focus on the balance between what we need today and the things we are excited to add and enhance tomorrow

Requirements

• Champion our development team principles: Clear and explicit communication, within the team and between teams across the company
• Develop iteratively, integrate early and often, and coordinate functional cross-team demos of the iterative releases
• End-to-end observable and traceable systems
• Plan, define, prioritize, and track design and development activities to meet milestones and support inter-team dependencies
• Develop and deploy both image processing workload and cluster orchestration software to bench, rack, and flight versions of the payload processing system
• Support integration of the payload subsystem itself (multiple compute elements therein), as well as integration with bus flight software, wideband RF communications systems, and ground and space-based image processing pipelines
• Support testing and qualification campaigns as well as on-orbit updates
• Optimize for space flight by selecting and applying lightweight but modern OSS frameworks and tools, applied in a bandwidth-conscious way to support on-orbit updates to any level of the system (BSP, OS, and applications)

Qualifications

• Bachelor’s degree in computer science, software engineering, aerospace engineering, or a related technical field
• High competency with both scripted and compiled/type-checked languages (e.g. Python, C++, Go)
• 10+ years total experience in professional software engineering
• Experience with deploying software to intermittently connected, resource constrained edge compute environments
• Containerized workload management (Kubernetes)
• Relevant experience will include OS level programming, containerized cluster management, embedded systems development, hardware acceleration, and a solid understanding of tradeoffs between features and footprint

Desired Qualifications

• NVIDIA Jetson and CUDA programming experience
• Container building and optimization experience
• Experience building and optimizing scalable distributed image processing workflows

Pay & Benefits

The salary range for this role is $170,000-$220,000 per year, depending on previous experience. Pay ranges are determined by role, level, location, and alignment with market data. Individual pay will be determined on a case-by-case basis and may vary based on the following considerations: interviews and an assessment of several factors that are unique to each candidate, job-related skills, relevant education and experience, certifications, abilities of the candidate and internal equity.

The Company reserves the right to modify or change these benefits at any time.