Ml / Ai Software Engineer, Electronic Warfare

vor 2 Wochen

Sydney, Österreich Tideri Jobbörse Vollzeit

ML / AI Software Engineer, Electronic Warfare DroneShield is a global provider of counterdrone defense solutions, specializing in C-UxS AI, RF sensing, AI/ML, Sensor Fusion, Rapid Prototyping & MIL-SPEC manufacturing.

DroneShield Ltd (ASX:DRO) is an Australian/US publicly listed company focusing on RF sensing, Artificial Intelligence and Machine Learning, Sensor Fusion, Electronic Warfare, Rapid Prototyping and MIL-SPEC manufacturing.

Our capabilities are used to protect Military, Government, Law Enforcement, Critical Infrastructure, Commercial and VIPs throughout the world.

Through our team of engineers, we offer customers bespoke solutions and off-the-shelf products designed to suit a variety of terrestrial, maritime or airborne platforms.

Job Description DroneShield is seeking a dedicated ML/AI Software Engineer to join the development team in Sydney.
The position will report to the RF AI Team Lead.
The role is centered around assisting DroneShield to develop and improve its machine learning technology in the radio frequency domain.

Responsibilities, Duties and Expectations
Generate procedures and tests to capture quality sensor data to leverage in machine learning models.
This will include field testing and site/client site data capture.
Manage multi-domain data sets, data preparation, and labelling.
Develop models to improve accuracy, efficiency, output, and insights.
Assist the software development team to implement models and provide input into how the output from this data is displayed to the user (GUI).
Document and maintain the code base supporting machine learning applications.
Assist the software/hardware development teams to make informed decisions about the future direction of a complex deployed system design.
Qualifications BS degree in Computer Science, Mathematics, or similar technical field of study or equivalent practical experience.
Experience with C++ development and Python scripting.
Familiarity with machine learning libraries like Keras, TensorFlow or PyTorch, and Scikit-learn (Sklearn).
Ability to develop, benchmark and optimize machine learning models.
Ability to deploy and maintain production software systems running on embedded platforms.
Familiarity with heterogeneous computing principles and CUDA programming is favorable.
Familiarity with signal processing techniques is favorable.
Proficiency in collecting, curating, and pre-processing large amounts of data from different domains and with different latency requirements.
Ability to translate business logic and objectives into briefs, executing on those ideas by generating well-functioning analytical models.
Ability to work in a multidisciplinary team, communicating effectively with engineers from non-software/data science backgrounds.
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