Machine Learning Engineer

Intecs Engineering Spa is an Italian company specialized in the design and development of applications, tools, software, hardware components and products for the Aerospace, Defence, Transport, Railway, Telecommunications and Smart Systems markets in collaboration with the main Industries, Organizations, Universities and European and Italian Research Laboratories. This role is open to candidates that are willing to relocate in Pisa (Italy). As a Machine Learning Engineer, you will be involved in challenging problems in different areas including, for example: (i) Detection and Tracking of targets in war scenarios based on optical and infrared on-ground cameras with edge processing constraints, as well as data fusion of detection metadata with other data sources (e.g. radar) (ii) quality improvement and super-resolution of on-ground/aerial images from electro-optical sensors based on GANs (iii) Improvement of our company solution for car parking monitoring based on car detection from RTSP streams and edge processing, (iv) development of our company counter-UAS technology solution for drone detection based on a on-ground optical/infrared camera for 360-degree surveillance and a zoomed camera. Main Responsibilities Manage the whole software lifecycle, with a focus in deployment and production monitoring for cutting-edge Machine Learning, Computer Vision and Deep learning solutions Write software that ensures high accuracy and speed performances, maintainability, scalability, reusability and reliability upon deployment Manage technical projects with limited supervision Interact with backend and frontend engineers for integration of the AI-based solution in the whole software infrastructure Read scientific research papers, re-implement them and customize solutions according to company needs and customer requirements Prepare presentations and participate in technical meetings, with possibility of short travels in Italy and/or abroad Draft technical documentations Draft technical proposals for national and international research initiatives Must Have Good knowledge of Python Hands-on experience in the main packages for Machine Learning: numpy, pandas, matplotlib, sklearn Hands-on experience in the framework Tensorflow or, in alternative, in PyTorch Good background in Statistics and Machine Learning (classification, regression and clustering) and in Convolutional Neural Networks for classification and detection tasks Hands-on experience with Docker container and conda environments Hands-on experience in Data Versioning (e.g. DVC) and CI/CD pipelines (e.g. Jenkins) Hands-on experience in network optimization techniques for edge processing (quantization, pruning, model compression) via TensorRT library Master Degree in Computer Science or Computer Engineering with a degree grade of at least 103/110 Ability to present the results of the work to both technical and not technical audiences Good knowledge of the English language (at least B2), both written and oral Willingness to learn and ability to adapt to changing and challenging scenarios Ability to work independently and in a team Availability to work in the Defence field Preferred At least 2 years work experience in an industrial setting as a Machine Learning Engineer Hands-on experience in inference on NVIDIA Jetson boards (e.g. Orin Nano, Xavier, TX2) or alternatively, on FPGA Hands-on experience in docker container orchestration based on Kubernetes Hands-on experience with GStreamer for reading from a camera and streaming to a remote endpoint Hands-on experience in the design of REST APIs by means of Flask or FastAPI Practical experience in C++ and object oriented programming What we offer Work with a team of professionals with different background and experience Face technical problems in different technology domains from Defence and Space to Railway and Automotive Take on more responsibility and grow professionally Present projects to workshops Hybrid work (up to 60%) after an initial period of candidate assessment Restaurant ticket of 8 euros for all working days #J-18808-Ljbffr