Edge Deployment Engineer (AI & Embedded Systems)

Hybrid opportunity in Zaragoza, fixed-term contract until 30th June 2026. What You'll Do:

• Implement and optimise deep-learning models for edge hardware.
• Reduce model size and latency using compression/quantisation.
• Work hands-on with embedded systems and systems programming.
• Write high-performance code in Python, C, or C++.
• Conduct performance profiling on diverse embedded architectures (ARM, GPUs).
• Integrate ML models into final products through team collaboration.
• Maintain development standards: Git, testing, and CI/CD pipelines.

• Bachelor's degree or higher in Computer Science, Electrical Engineering, Physics, or related field; or equivalent industry experience.
• 3-5 years of hands-on experience in embedded systems, firmware development, or systems programming.
• Demonstrated experience optimizing machine learning models for deployment on constrained devices.
• Strong proficiency in Python, C, or C++; experience with system-level programming languages is essential.
• Solid understanding of quantisation techniques and model compression strategies; experience with inference optimisation frameworks (TensorRT, ONNX Runtime, LLM, vLLM, or equivalent).
• Familiarity with embedded architectures: ARM processors, mobile GPUs, and AI accelerators.
• Strong fundamentals in computer architecture, memory management, and performance optimisation.
• Experience with version control (Git), testing frameworks, and CI/CD pipelines.
• Excellent communication and collaboration skills in cross-functional teams., * Master's degree in Computer Science, Electrical Engineering, or related field.
• Hands-on experience with large language model inference and deployment.
• Experience optimizing neural networks using mixed-precision computation or dynamic quantisation.
• Familiarity with edge computing frameworks such as NVIDIA's Triton Inference Server or similar platforms.
• Background in mobile or IoT development.
• Knowledge of hardware acceleration techniques and specialised instruction sets (SIMD, NPU-specific optimisations).
• Contributions to open-source embedded AI or ML optimisation projects.
• Experience with real-time operating systems or embedded Linux environments.

• Competitive salary, with a signing bonus and a retention bonus at the end of the contract.
• Hybrid role with flexible working hours. A relocation package is available if needed.
• Fast-scaling company committed to equal pay, diversity, and an inclusive culture. International exposure in a multicultural, cutting-edge environment.