Edge Deployment Engineer (AI & Embedded Systems)
Location: Hybrid opportunity in Zaragoza (remote flexibility available)
Contract: Fixed-term contract until 30th June 2026
Company: European deep‑tech leader in quantum and AI, transforming AI by compressing large language models by up to 95% and cutting inference costs by 50–80%.
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.
Required Qualifications
* 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.
Preferred Qualifications
* 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.
Compensation & Benefits
* Competitive salary, 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.
How to Apply
Apply directly through LinkedIn or send your CV to george@eu-recruit.com.
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