Machine Learning Engineer
As a member of the HomeX R&D team, you will be responsible for the implementation and operation of AI solutions targeting the Home Services domain. Your focus will be on the development of Deep Neural Networks targeting real-world data. You will collaborate with others to create a multimodal AI system that understands repair and maintenance in the home.
You are a Machine Learning Engineer with industry experience creating simple solutions to complex problems. You are comfortable applying state-of-the-art machine learning techniques to new problem spaces, resulting in practical solutions that solve real problems for real customers. You are able to deliver solutions as a series of incremental improvements that continually add value.
You understand the principles of well-architected software and systems including operational excellence, scalability and resilience. Your work reflects high standards of quality and robustness in order to ensure the highest level of service for customers in a production setting.
You are a hands-on contributor who embodies the value of teamwork, discussion and consensus-building. You are able to drive initiatives forward both as an individual and as part of a team. You can collaborate with colleagues from a variety of backgrounds such as designers, engineers, product managers.
You enjoy working in a scrappy, startup environment where you do what’s needed to get the job done. Adapting to change is something you thrive on. You embrace new challenges. Unexplored problem spaces pique your curiosity and drive you to innovate on behalf of our customers at all times.
- Theoretical understanding of Machine Learning, evidenced by a Master's degree (or PhD) in AI, ML, Computer Science, Data Science or a closely related field.
- Industry experience using Machine Learning to deliver value in a production environment.
- Knowledge of the entire machine learning lifecycle (design, training, deployment, evaluation and updating).
- Proficiency in deep-learning architectures and techniques such as convolutional neural networks, transformers, transfer learning, semi-supervised learning and data augmentation.
- Working knowledge of deployment in cloud-based environments (preferably Google Cloud Platform).
- Proficiency in Python and a popular ML framework (such as Keras, Pytorch or Tensorflow).
- Familiarity with Deep Learning approaches to computer vision tasks such as image classification, object detection, instance segmentation, scene reconstruction and scene understanding.
- Experience developing Machine Learning pipelines in the cloud.
Nice to Have
- Familiarity with developing and operating ML models on edge devices.
- Familiarity with the Semantic Web (RDF, OWL, SPARQL).
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