Software Engineer for Edge AI in Space

We are seeking a skilled Software Engineer to join our team and contribute to the development of cutting-edge Edge AI solutions in space. As a member of our team, you will work on designing, implementing, and optimizing embedded software solutions for AI/ML workloads on NVIDIA, Xilinx, and Microchip platforms.

• You will be responsible for building and maintaining Linux-based environments, writing integration libraries, and enabling containerized deployment of applications on constrained edge devices.
• You will collaborate with Data Science teams to enable deployment on resource-constrained hardware and work with C, C++, and Python to build and optimize drivers, OS-level services, and low-level libraries for ML acceleration.

This is an exciting opportunity to shape the future of real-time satellite intelligence and contribute to the development of breakthrough technology that enables autonomous systems in space.

This full-time remote role requires excellent technical communication skills to collaborate across global teams and partner satellite operators. You will work under the Head of Engineering and the CTO, contributing not just to managing development kits and system integration but also to actively developing the embedded software stack that powers our edge intelligence solutions in space.

• Configure and manage Linux environments (Ubuntu and custom distributions), including kernel modules, system tuning, cross-compilation, and deployment scripts.
• Integrate ML models into embedded systems; collaborate with Data Science teams to enable deployment on resource-constrained hardware.
• Work with C, C++, and Python to build and optimize drivers, OS-level services, and low-level libraries for ML acceleration.
• Develop, test, and optimize Dockerized applications for AI/ML workloads, ensuring portability, efficiency, and compliance with partner satellite company requirements.
• Manage and maintain Edge AI development kits (NVIDIA Jetson, Xilinx platforms, etc.), ensuring smooth workflows for in-orbit simulation and testing.
• Leverage CUDA, cuDNN, TensorRT (NVIDIA) and Vitis AI / OpenCL (Xilinx) to accelerate workloads.
• Interface with satellite payload hardware and ground systems; debug integration issues across hardware, OS, and application layers.
• Develop pipelines for automated testing and deployment of embedded software builds.
• Maintain clear documentation of configurations, workflows, and deployment practices; support partner satellite companies in deploying our dockerized products.
• Collaborate using Git/GitHub, code review best practices, and working in distributed development teams.

• Knowledge of embedded networking (TCP/IP, UDP, MQTT, gRPC), especially for low-bandwidth or intermittent links.
• Familiarity with secure boot, encryption standards, and container security (bonus if experience with Post-Quantum Cryptography concepts).
• Experience with ARM/x86 toolchains, cross-compilation, and Yocto/OpenEmbedded.
• Ability to profile embedded workloads and optimize for power, latency, and throughput constraints.
• Scripting & Automation: Bash, Python, and Ansible (good to have) for automating builds, deployments, and system monitoring.
• Debugging & Profiling Tools: GDB, Valgrind, perf, Nsight, and similar tools for low-level performance analysis.
• Satellite/Space Domain Exposure (Nice-to-Have): Experience with telemetry, satellite communications, or aerospace-grade systems.

• Reliable, efficient Dockerized builds delivered to partner satellites.
• Smooth operation and maintenance of NVIDIA/Xilinx dev kits for AI/ML use cases.
• Reduced latency and improved efficiency of ML models on embedded devices.
• Strong cross-team collaboration leading to faster turnaround on edge deployment milestones.

• Bachelor's/Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or equivalent.
• 3–8 years of hands-on experience in embedded software development.
• Proficiency in C, C++, and Python.
• Deep knowledge of Linux (system internals, drivers, kernel tuning).
• Proven experience with Docker and containerized workflows.
• Experience with NVIDIA Jetson, CUDA, TensorRT, and Xilinx Vitis AI / OpenCL.
• Familiarity with edge AI/ML workloads, model deployment, and performance optimization.
• Strong debugging, problem-solving, and communication skills.
• Ability to work independently in a remote-first environment, coordinating across time zones.

At Little Place Labs, we value diversity and inclusion. We welcome applicants from all backgrounds and believe that everyone deserves equal opportunities.