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Our inference systems need to act fast, taking 1.4 million decisions a day with incredibly low error rates and running on minimal edge resources. As a lead engineer for AI, you will develop and implement novel material recognition solutions using data deciphered from multi-energy X-ray during high-speed industrial inspection, where even small anomalies can pose high safety risks.
You will want to join a small, multidisciplinary team dedicated to commercialising cutting-edge X-ray inspection technologies with genuine market benefits.
You will have a passion for hands-on projects and building scalable solutions.
You will be curious about how things work and how to maximize their performance.
You will enjoy working both autonomously and collaboratively alongside business leadership and PhD colleagues in microelectronics and high-energy physics in a dynamic technology spin-out.
Responsibilities
* Lead the deployment and optimization of edge-based neural networks for real-time inference on resource-constrained devices.
* Design and implement scalable deployment pipelines, including model conversion, quantization, and validation for GPU-based edge platforms.
* Monitor performance metrics and conduct rigorous testing to ensure reliability, low latency, and power efficiency in deployed models.
* Collaborate with cross-functional teams to evaluate new hardware platforms and deployment tools for future model compatibility and performance gains.
* Maintain documentation and best practices for deployment processes, toolchains, and model optimization techniques to support long-term scalability.
* Bachelor's or Master’s degree in Computer Science, Electrical Engineering, AI Engineering, or a related technical field (PhD is a plus).
* Strong experience deploying deep learning models in production environments, with emphasis on high-throughput, low-latency inference.
* Hands-on experience with modern ML frameworks such as PyTorch and Keras.
* Proficiency in modern C++.
* Experience with the C++ API of PyTorch (LibTorch) for deploying models in production or embedded environments.
* Proven expertise in optimizing models for GPU inference, including TensorRT, ONNX Runtime, TorchScript, or TVM.
* Knowledge of batch optimization and asynchronous execution.
* Strong collaboration skills to partner with AI researchers in bridging research and real-world embedded applications.
* Knowledge of high energy physics and X-ray behaviors.
* Understanding of image reconstruction from multi-level and multi-projection data sets.
* Good communication skills, both as a team player and critical thinker.
* Effective problem-solving skills, anticipating issues and creating solutions.
* Strong project management, presentation, and report-writing skills (professional English).
Start Date : from June 2025
Role Type : Full Time
Seniority level
* Mid-Senior level
Employment type
* Full-time
Job function
* Engineering and Information Technology
* Manufacturing
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