Embedded AI Systems Engineer

This is a fully remote position, open to applicants in Spain.

📋 Description

• Design and create embedded AI platforms using virtualization and hypervisor technologies, including architectures based on Xen.

• Develop and enhance AI/ML workloads for embedded and edge computing settings.

• Integrate Linux, RTOS, and mixed-criticality workloads within virtualized embedded systems.

• Configure and fine-tune Xen Hypervisor environments for ARM, x86, and embedded SoC platforms.

• Support AI acceleration technologies such as GPUs, NPUs, FPGAs, and hardware-assisted virtualization.

• Implement secure workload isolation, resource partitioning, and fault-tolerant architectures for embedded systems.

• Develop low-level software components, including drivers, BSPs, device tree configurations, and bootloader integrations.

• Collaborate with hardware, platform, networking, and AI software teams to facilitate scalable deployments of embedded AI.

• Optimize system performance, boot times, memory allocation, interrupt latency, and real-time responsiveness.

• Support containerization, VM orchestration, and automation for edge deployment in embedded systems.

• Engage in debugging, profiling, benchmarking, and performance tuning across embedded platforms.

• Contribute to open-source projects and virtualization-related engineering efforts where applicable.

⛳️ Requirements

• Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.

• Over 3 years of experience in embedded systems, Linux platform engineering, or virtualization technologies.

• Proficient in C/C++, Python, and embedded software development.

• Preferred familiarity with Xen Hypervisor, KVM, QEMU, or similar virtualization technologies.

• Experience with ARM-based embedded platforms and concepts of the Linux kernel.

• Knowledge of AI/ML frameworks such as TensorFlow, PyTorch, ONNX, or TensorRT.

• Understanding of embedded networking, memory management, interrupt handling, and low-level system architecture.

• Experience with Yocto, Buildroot, Docker, Kubernetes at the edge, or embedded CI/CD workflows is advantageous.

• Familiarity with real-time systems, functional safety, or automotive embedded environments is preferred.

• Strong debugging, problem-solving, and performance optimization skills.

🏝️ Benefits

• Flexible work arrangements

• Professional development opportunities