Senior Machine Learning Engineer 資深機器學習工程師

▍About AlfredCamera

From day one, we’ve been guided by the belief that home security should be made accessible to everyone. We started out by offering a software solution that turns your old phone into a security camera in 2014. Since then, this simple but powerful idea has become the go-to security app for more than 50 millions families worldwide. 

In 2022, we launched our first hardware product, AlfredCam, which opened up an exciting new chapter for us. And 2023 is going to be an even more exhilarating year, as we are taking more proactive actions to respond to our consumers and expanding our presence in more countries. If you want to be a game changer for one of the most trusted brands worldwide, we have the right opportunity for you!

▍Your Role

As a Machine Learning Engineer specializing in both Computer Vision and Audio, you will play a crucial role in developing cutting-edge solutions for a wide range of applications. Your expertise will be instrumental in creating and implementing machine learning models and algorithms that cover areas such as image classification, object detection, audio classification, audio enhancement, semantic segmentation, image generation, and video analysis.

As part of AlfredCamera's mission to make home security accessible to everyone, you will be a game-changer, driving innovation and contributing to the success of one of the most trusted brands worldwide. Join us in this captivating journey where your expertise will have a significant impact on enhancing the safety and security of millions of families around the globe.

▍Responsibility

• Collaborate closely with esteemed computer vision and audio researchers at AlfredCamera, integrating cutting-edge techniques into solutions that drive innovation in the home security industry.
• Seamlessly weave machine learning models into production systems alongside software engineers, ensuring seamless scalability, real-time performance, and robustness for both computer vision and audio tasks.
• Prepare and meticulously process large-scale datasets to train and evaluate machine learning models, ensuring the highest standards of security for our users.
• Optimize and fine-tune machine learning models to enhance accuracy, efficiency, and adaptability in both computer vision and audio domains, validating their capabilities through rigorous experimentation and performance evaluations.
• Immerse yourself in the latest advancements in computer vision and audio fields, actively contributing to the research community, and exploring uncharted territories of innovation.
• Translate strategic business requirements into actionable machine learning projects, ensuring seamless alignment with customer needs and expectations, and driving the growth and success of AlfredCamera as one of the most trusted and innovative brands globally.

▍Minimum qualifications

• A profound background in machine learning and deep learning, complemented by a deep-rooted understanding of computer vision and audio processing techniques.
• A proven track record of developing and deploying machine learning models for computer vision and audio applications, showcasing your expertise in convolutional neural networks (CNNs), generative models, and audio signal processing techniques.
• Exceptional programming prowess in Python, along with hands-on experience in industry-leading deep learning frameworks such as TensorFlow and PyTorch, for both computer vision and audio tasks.
• An encyclopedic knowledge of computer vision concepts, image processing, visual recognition algorithms, as well as audio signal processing algorithms.
• A familiarity with indispensable computer vision libraries such as OpenCV and scikit-image, coupled with an affinity for audio-related libraries such as Librosa and Essentia.
• A tenacious problem-solving acumen, enabling you to dissect complex visual and audio data with ease.
• Superb communication and teamwork skills, facilitating seamless collaboration with cross-functional teams and a proven track record of delivering successful machine learning models and algorithms in commercial environments.
• A profound grasp of edge device deployment is considered a valuable asset.

▍Preferred qualifications

• An MS or PhD in related areas such as Computer Science, EE, Mathematics, or Statistics.
• Experience building production-level systems using ML/DL techniques for both computer vision and audio domains.
• Expertise in optimizing models for edge devices.

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